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DOE OSTI2020

Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics

We develop physics-constrained and control-oriented predictive deep learning models for the thermal dynamics of a real-world commercial office building. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our model mimics the structure of the building thermal dynamics model and leverages penalty methods to model inequality constraints. Additionally, we use constrained matrix parameterization based on the Perron-Frobenius theorem to bound the eigenvalues of the learned network weights. We interpret the stable eigenvalues as dissipativeness of the learned building thermal model. We demonstrate the effectiveness of the proposed approach on a dataset obtained from an office building with $20$ thermal zones.

Building Energy, structured neural networks↗

DOE OSTI2022

Posterior Regularized Bayesian Neural Network

Traditional NNs often lack the ability for uncertainty quantification. Bayesian NNs(BNNs) could help measure the confidence level by using distributions in NNs modeling. Besides, knowledge is commonly available and could improve the performance of BNNs if it can be properly incorporated. In this work, we propose a novel Posterior-Regularized BNN(PR-BNN) model by incorporating soft and hard constraints as a posterior regularization term. We also propose an augmented Lagrangian method and stochastic optimization algorithm for efficient updating via Monte Carlo sampling. The simulations and case studies for solar PV plants have shown the performance improvement of the proposed model over traditional BNNs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science

In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational models. Previous work in informative path planning, adaptive sampling, and experimental design have shown compelling results, largely in static environments, using data-driven models and information-based rewards. However, these methodologies do not trivially extend to expeditionary science in spatiotemporal environments: they generally do not make use of scientific knowledge such as equations of state dynamics, they focus on information gathering as opposed to scientific task execution, and they make use of decision-making approaches that scale poorly to large, continuous problems with long planning horizons and real-time operational constraints. In this work, we discuss these and other challenges related to probabilistic modeling and decision-making in expeditionary science, and present some of our preliminary work that addresses these gaps. We ground our results in a real expeditionary science deployment of an autonomous underwater vehicle (AUV) in the deep ocean for hydrothermal vent discovery and characterization. Our concluding thoughts highlight remaining work to be done, and the challenges that merit consideration by the reinforcement learning and decision-making community.

Preston, Victoria↗

NASA NTRS1990

Autonomous power expert system

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning.

Ringer, Mark J.↗

NASA NTRS1990

Graphical explanation in an expert system for Space Station Freedom rack integration

The rationale and methodology used to incorporate graphics into explanations provided by an expert system for Space Station Freedom rack integration is examined. The rack integration task is typical of a class of constraint satisfaction problems for large programs where expertise from several areas is required. Graphically oriented approaches are used to explain the conclusions made by the system, the knowledge base content, and even at more abstract levels the control strategies employed by the system. The implemented architecture combines hypermedia and inference engine capabilities. The advantages of this architecture include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. The graphical techniques employed range from simple statis presentation of schematics to dynamic creation of a series of pictures presented motion picture style. User models control the type, amount, and order of information presented.

Craig, F. G.↗

NASA NTRS1990

Autonomous power expert system

Ringer, Mark J.↗

NASA NTRSDate not supplied

X-Ray Computed Tomography During Preliminary Examination of Apollo Drive Tube 73001

Introduction: Starting in 2019, the Apollo Next Generation Sample Analysis (ANGSA) Program has enabled consortium studies of specially curated Apollo samples that were previously unstudied (or under studied). This began with unsealed core tube 73002 [1,2] that is the upper part of a station 3 double drive tube. More recently the pro-gram extended to the study of a variety of frozen Apollo 17 samples [3], as well as the gas extraction [4] and dis-section [5] of 73001, the lower half of the station 3 double drive tube, that was sealed under vacuum on the Moon. In this abstract we will examine the role of X-ray Computed Tomography (XCT) during the preliminary examination process for sealed core 73001, including: (1) engineering scans to aid in understanding the gas extraction process, whole-core scanning prior to opening to inform extrusion and dissection work, and (3) individual particle scanning to characterize rock fragment lithologies for follow on studies. Methodology: Sample 73001 is a 33 cm long, 4 cm diameter regolith sample collected inside a drive tube (~1 mm aluminum walls). That drive tube was sealed inside a 0.5 mm thick stainless steel (SS) Core Sample Vacuum Container (CSVC). XCT scans for engineering purposes were done on the Nikon XTH 320 system at Johnson Space Center using the 225 kV multi-metal reflection source at 215 kV, 179 A, and a 38.49 m voxel size. Individual >4 mm particles separated from the core during processing (then triply sealed in Teflon bags) were also scanned at JSC using the 180 kV source at 90 kV, 33 A, and a 2.98 – 10.65 m voxel size. Whole-core scans were done at the University of Texas High-Resolution X-ray Computed Tomography Facility (UTCT) on the 225 kV reflection source on the North Star Imaging cabinet XCT system. These scans included: (1) a series of 9 overlapping super-resolution scans each covering a ~4 cm length of the tube at 190 kV, 180 µA, and a 12.9 µm voxel size and (2) a lower resolution continuous helical scan of the entire core at 190 kV, 180 µA, and a 51.8 µm voxel size. Progress and Results: Before piercing and extracting the gas from sample 73001, an XCT scan of the bottom portion of the CSVC was used to confirm the location of the Teflon cap on the inner drive tube, to ensure it was not accidentally pierced during gas extraction. Similarly, after piercing, the bottom and top portions of the CSVC were scanned in order to capture engineering knowledge about the results of the piercing process, as well as the metal knife edge vacuum seal (SS into In-Ag alloy). Both scans will provide constraints on future work of this type, particularly for samples collected during the Artemis mission. Another finding from these "engineering" scans was that the device in the drive tube that immobilizes the regolith (the keeper) was not seated in the tube properly. This meant that (1) the drive tube could not be removed from the CSVC for the trip to UTCT, and (2) the procedure for opening and extruding the drive tube had to be modified. Had either of these things not been known prior to opening the CSVC, it could have led to an inability to XCT scan the whole core at high resolution and/or potential disruption of the core stratigraphy during extrusion. At UTCT, the entire length of the core was scanned at high resolution (12.9 microns per voxel). This scan serves multiple purposes: (1) A lower resolution (and uncorrected) version of these scans stitched together was used to help inform the processors of potential pitfalls during extrusion and dissection; and (2) the full resolution corrected data will serve as the permanent in situ record of the stratigraphy of the sample and will enable future researchers to perform a variety of analyses. So far, 92 of the 121 >4 mm particles separated during dissection pass 1 of sample 73001 have been individually scanned. These scans clearly show the lithology of each particle while keeping the particles in pristine condition. Because of the dust adhering to particle exteriors it would otherwise be impossible to determine lithologies in a non-contaminating way. Thus far the types of lithologies seen in sample 73001 (e.g., regolith breccias, impact-melt breccias, agglutinates, and basalts) are similar to those previously identified in sample 73002 [2]. By the time of the meeting, all particles from all 3 dissection passes will have been scanned and statistics on the different lithologies in 73001 compiled. References: [1] Shearer et al. (2020) 51st LPSC, abstract 1181. [2] Zeigler et al. (2020) 51st LPSC, abstract 3023. [3] Kent et al. (2022) This Volume. [4] McDonald et al. (2022) European Lunar Symposium. [5] Gross et al (2022) This volume.

Moon↗

NASA NTRS2012

Direct Imaging of Extra-Solar Planets in Star Forming Regions: Lessons Learned from a False Positive Around IM Lup

Context. Most exoplanet imagers consist of ground-based adaptive optics coronagraphic cameras which are currently limited in contrast, sensitivity and astrometric precision, but advantageously observe in the near-infrared window (1 5 m). Because of these practical limitations, our current observational aim at detecting and characterizing planets puts heavy constraints on target selection, observing strategies, data reduction, and follow-up. Most surveys so far have thus targeted young systems (1 100 Myr) to catch the putative remnant thermal radiation of giant planets, which peaks in the near-infrared. They also favor systems in the solar neighborhood (d less than 80 pc), which eases angular resolution requirements but also ensures a good knowledge of the distance and proper motion, which are critical to secure the planet status, and enable subsequent characterization. Aims. Because of their youth, it is very tempting to target the nearby star forming regions, which are typically twice as far as the bulk of objects usually combed for planets by direct imaging. Probing these interesting reservoirs sets additional constraints that we review in this paper by presenting the planet search that we initiated in 2008 around the disk-bearing T Tauri star IM Lup, which is part of the Lupus star forming region (140-190 pc). Methods. We show and discuss why age determination, the choice of evolutionary model for both the central star and the planet, precise knowledge of the host star proper motion, relative or absolute (between different instruments) astrometric accuracy (including plate scale calibration), and patience are the key ingredients for exoplanet searches around more distant young stars. Results. Unfortunately, most of the time, precision and perseverance are not paying off: we discovered a candidate companion around IM Lup in 2008, which we report here to be an unbound background object. We nevertheless review in details the lessons learned from our endeavor, and additionally present the best detection limits ever calculated for IM Lup. We also accessorily report on the successful use of innovative data reduction techniques, such as the damped-LOCI and iterative roll subtraction.

D Mawet↗

DOE OSTI2024

Direct Geologic Constraints on the Timing of Late Holocene Ice Thickening in the Amundsen Sea Embayment, Antarctica

Abstract Constraining past West Antarctic Ice Sheet (WAIS) change helps validate numerical models simulating future ice sheet dynamics. Following rapid deglaciation during the mid‐Holocene, ice near Thwaites Glacier was ∼35 m thinner than present; however, the timing of ice regrowth to its present configuration remains unknown. To fill this knowledge gap, we present cosmogenic nuclide exposure ages of cobbles from the surface of a moraine situated between Thwaites and Pope glaciers. We infer that the moraine formed and stabilized in the Late Holocene (∼1.4 ka) when a small glacier thickened. We also present a novel reconstruction of WAIS volume constrained by sea‐level data, which demonstrates that moraine formation coincided with a large‐scale WAIS readvance. Our new geologic constraints will help inform models of the solid Earth response to surface mass loading, improving our understanding of ice sheet dynamics in a vulnerable part of WAIS.

Nichols, Keir A. [Imperial College London London U↗

DOE OSTI2024

Robust wind farm layout optimization

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.

17 WIND ENERGY↗

DOE OSTI2021

Scalable Pattern Matching in Metadata Graphs via Constraint Checking

Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗

NASA NTRS1990

A knowledge-based approach to improving optimization techniques in system planning

A knowledge-based (KB) approach to improve mathematical programming techniques used in the system planning environment is presented. The KB system assists in selecting appropriate optimization algorithms, objective functions, constraints and parameters. The scheme is implemented by integrating symbolic computation of rules derived from operator and planner's experience and is used for generalized optimization packages. The KB optimization software package is capable of improving the overall planning process which includes correction of given violations. The method was demonstrated on a large scale power system discussed in the paper.

Momoh, J. A.↗

NASA NTRS1993

An integrated knowledge system for the Space Shuttle hazardous gas detection system

A computer-based integrated Knowledge-Based System, the Intelligent Hypertext Manual (IHM), was developed for the Space Shuttle Hazardous Gas Detection System (HGDS) at NASA Marshall Space Flight Center (MSFC). The IHM stores HGDS related knowledge and presents it in an interactive and intuitive manner. This manual is a combination of hypertext and an expert system which store experts' knowledge and experience in hazardous gas detection and analysis. The IHM's purpose is to provide HGDS personnel with the capabilities of: locating applicable documentation related to procedures, constraints, and previous fault histories; assisting in the training of personnel; enhancing the interpretation of real time data; and recognizing and identifying possible faults in the Space Shuttle sub-systems related to hazardous gas detection.

Lo, Ching F.↗

NASA NTRS1991

Generalizing Atoms in Constraint Logic

This paper studies the generalization of atomic formulas, or atoms, that are augmented with constraints on or among their terms. The atoms may also be viewed as definite clauses whose antecedents express the constraints. Atoms are generalized relative to a body of background information about the constraints. This paper first examines generalization of atoms with only monadic constraints. The paper develops an algorithm for the generalization task and discusses algorithm complexity. It then extends the algorithm to apply to atoms with constraints of arbitrary arity. The paper also presents semantic properties of the generalizations computed by the algorithms, making the algorithms applicable to such problems as abduction, induction, and knowledge base verification. The paper emphasizes the application to induction and presents a pac-learning result for constrained atoms.

Page, C. David, Jr.↗

NASA NTRS2005

Advanced Electronics Technologies: Challenges for Radiation Effects Testing, Modeling, and Mitigation

Emerging Electronics Technologies include: 1) Changes in the commercial semiconductor world; 2) Radiation Effects Sources (A sample test constraint); and 3) Challenges to Radiation Testing and Modeling: a) IC Attributes-Radiation Effects Implication b) Fault Isolation c) Scaled Geometry d) Speed e) Modeling Shortfall f) Knowledge Status

LaBel, Kenneth A.↗

NASA NTRS2009

Prominence Structure and Dynamics

Nonerupting prominences are not dull, static objects. Rather, they are composed of fine-scale blobs and threads that are highly dynamic, often appearing to travel in opposite directions on adjacent tracks (denoted counterstreaming). Because the plasma is largely constrained to travel along the magnetic field, these cool, dense features can serve as tracers of the prominence magnetic structure, a valuable resource in view of the long-standing difficulty of observing the coronal field. Conversely, greater understanding of the fundamental magnetic geometry of filament channels can provide important constraints on the physical processes governing the accumulation, support, motion, and eruption of the cool plasma. Despite over a century of detailed observations, large gaps remain in our knowledge of filament channel/plasma formation and evolution. Resolving these issues will shed light on the physics of coronal heating, helicity transport throughout the solar cycle, and the origins of eruptive activity on the Sun. I will discuss the leading models for the magnetic and plasma structure, and outline how new observations and theory /modeling could solve long-standing uncertainties regarding this majestic solar phenomenon.

Karpen, Judy T.↗