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NASA NTRSDate not supplied

Design and Testing of the BionicWingSat in a Zero-g Flight Campaign - A 2U-CubeSat with Deployable, Biologically-Inspired Wings

In this paper, recent developments in the design, manufacturing, and testing of a novel deployable structure with several potential applications in space will be described. Through a cooperative effort of the German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA), a biologically inspired structurally integrated membrane featuring distributed functional elements has been developed and tested in a 2U CubeSat called BionicWingSat. Such a membrane structure could be useful for several applications in which a relatively flat area is desirable such as solar sails, drag sails, or solar shades. For SmallSats and CubeSats, the design proposed also has the desirable property of being self-deploying without the need for powered deployment mechanisms. Building on previous work inspired by the wings of earwigs, the research presented in this paper includes structural design of self-deploying hinges, a survey of various advanced additive layer manufacturing (ALM) methods for making hinges, mechanical characterization of the hinges, and finite element analysis (FEA) of the hinges. In this work, the conflicting goals of maximizing deployed structural stiffness, maximizing deployed area, maximizing stowed packaging efficiency, and maximizing resistance to creep when stowed must be considered. The resulting design concept is a gossamer structure that cannot support its own weight in gravity. For this reason, a focus in this paper is on a parabolic flight test campaign in which 24 fully integrated wings on two BionicWingSats were tested in a microgravity environment. From this test campaign, several lessons were learned regarding the wing design and procedures for carrying out microgravity tests of this manner.

CubeSat↗

NASA NTRSDate not supplied

Design and Testing of the BionicWingSat in a Zero-g Flight Campaign - A 2U-CubeSat with Deployable, Biologically-Inspired Wings

CubeSat↗

NASA NTRS2021

A Sampling-Based Optimization Approach to Handling Environmental Uncertainty for a Planetary Lander

Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness

Chien, Steve↗

DOE OSTI2020

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models

Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizing the mutual information (MI) between the data and the model parameters. When the analytical expression of the MI is unavailable, e.g.,having implicit models with intractable data distributions, a neural network-based lower bound of the MI was recently proposed and a gradient ascent method was used to maximize the lower bound [1]. However, the approach in [1] requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models. In this work, we propose a hybrid gradient approach that leverages recent advances in variational MI estimator and evolution strategies (ES)combined with black-box stochastic gradient ascent (SGA) to maximize the MI lower bound. This allows the design process to be achieved through a unified scalable procedure for implicit models without sampling path gradients. Several experiments demonstrate that our approach significantly improves the scalability of BED for implicit models in high-dimensional design space.

Zhang, Jiaxin↗

DOE OSTI2022

Variable resolution Poisson-disk sampling for meshing discrete fracture networks

Here, we present the near-Maximal Algorithm for Poisson-disk Sampling (nMAPS) to generate point distributions for variable resolution Delaunay triangular and tetrahedral meshes in two and three-dimensions, respectively. nMAPS consists of two principal stages. In the first stage, an initial point distribution is produced using a cell-based rejection algorithm. In the second stage, holes in the sample are detected using an efficient background grid and filled in to obtain a near-maximal covering. Extensive testing shows that nMAPS generates a variable resolution mesh in linear run time with the number of accepted points. We demonstrate nMAPS capabilities by meshing three-dimensional discrete fracture networks (DFN) and the surrounding volume. The discretized boundaries of the fractures, which are represented as planar polygons, are used as the seed of 2D-nMAPS to produce a conforming Delaunay triangulation. The combined mesh of the DFN is used as the seed for 3D-nMAPS, which produces conforming Delaunay tetrahedra surrounding the network. Under a set of conditions that naturally arise in maximal Poisson-disk samples and are satisfied by nMAPS, the two-dimensional Delaunay triangulations are guaranteed to only have well-behaved triangular faces. While nMAPS does not provide triangulation quality bounds in more than two dimensions, we found that low-quality tetrahedra in 3D are infrequent, can be readily detected and removed, and a high-quality balanced mesh is produced.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer [Slides]

Project Goal: Use parametric Machine Learning methods in order to reduce memory requirements at checkpointing & restarting in the IMC simulations of Thermal Radiative Transfer using: Expectation Maximization and Weighted Gaussian Mixture Model-based approach for `particle-data compression', introduced in Plasma Physics to model Maxwellian particle distributions by Luis Chacon and Guangye Chen; Expectation Maximization with Weighted Hyper-Erlang Model in order to compress isotropic IMC particle data in the frequency domain; and Expectation Maximization and von Mises-Fisher Mixture Model for compression of anisotropic IMC particle data in the angular domain (work-in-progress).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Life-cycle analysis of integrated biorefineries with co-production of biofuels and bio-based chemicals: co-product handling methods and implications: Co-product handling methods and implications of life-cycle analysis of biofuel and bio-chemical co-producing biorefineries

New integrated biorefinery (IBR) concepts are being investigated to co-produce hydrocarbon fuels and high-value bio-based chemicals to improve the economic viability of IBRs, to enhance biomass resource utilization efficiencies, and to maximize potential greenhouse gas (GHG) emission reductions. Unlike fuel-only biorefineries, IBRs may co-produce a significant amount of bio-based chemicals, whose emission implications for specific biorefinery products and the biorefinery as a whole need to be evaluated. We discuss this in principle and apply three sets of co-product handling methods to conduct life-cycle analysis (LCA) of modeled IBRs with co-production of two bioproduct examples - succinic acid and adipic acid - alongside a renewable diesel blendstock fuel product. The LCA results for the specific co-product handling methods that were examined shed light on potential artifacts of product-specific LCA with selected co-product methods. We discuss the advantages and limitations of each method and conclude that (i) a system-level or 'black-box' LCA allocation method is too simplistic to reflect appropriately the GHG burdens of distinctly different processing trains for fuels and chemicals in the IBR context, and (ii) the displacement method is the only co-product handling method that accounts fully for the emission effects of both the fuel product and the non-fuel bio-based co-products in the IBRs within the context of the existing fuel-focused GHG regulatory framework. Alternatively, biorefinery system-level LCA combines benefits of individual products to offer a complete picture. This system-level LCA approach offers a holistic LCA without somewhat arbitrary decisions either on an allocation basis or by the selection of an evaluation metric based on specific products.

09 BIOMASS FUELS↗

DOE OSTI2023

Variance in offspring sex ratio and maternal allocation in a highly invasive mammal

Abstract Skewed sex ratios at birth are widely reported in wild populations, however, the extent to which parents are able to modulate the sex ratio of offspring to maximize their own fitness remains unclear. This is particularly true for highly polytocous species as maximizing fitness may include trade‐offs between sex ratio and the size and number of offspring in litters. In such cases, it may be adaptive for mothers to adjust both the number of offspring per litter and offspring sex to maximize individual fitness. Investigating maternal sex allocation in wild pigs ( Sus scrofa ) under stochastic environmental conditions, we predicted that under favorable conditions, high‐quality mothers (larger and older) would produce male‐biased litters and invest more in producing larger litters with more males. We also predicted sex ratio would vary relative to litter size, with a male‐bias among smaller litters. We found evidence that increasing wild boar ancestry, maternal age and condition, and resource availability may weakly contribute to male‐biased sex ratio, however, unknown factors not measured in this study are assumed to be more influential. High‐quality mothers allocated more resources to litter production, but this relationship was driven by adjustment of litter size, not sex ratio. There was no relationship between sex ratio and litter size. Collectively, our results emphasized that adjustment of litter size appeared to be the primary reproductive characteristic manipulated in wild pigs to increase fitness rather than adjustment of offspring sex ratio.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Radial storage efficiency for CO 2 injection: Quantifying effectiveness of local flow control methods

Basin-scale geologic carbon sequestration will require hundreds of injection wells, each of which has costs related to property rights and regulatory requirements that correlate with the areal size of the carbon dioxide plume. These surface-footprint-related issues motivate maximizing storage efficiency radially away from each well. Radial storage efficiency (RSE) is defined here as the ratio of the volumetrically weighted carbon dioxide free-phase saturation within a given radius away from the injection well to the available pore space within that same radius. Maximizing RSE effectively minimizes the radial extent of the CO 2 plume. Optimizing RSE around individual wells requires local flow control injection strategies that can increase the uniformity of the filling of pore space around the well over the entire length of the perforated injection interval despite differences in local formation transmissivity. The goal of uniform filling of the storage reservoir starting from the well outward is to maximize carbon dioxide sweep and trapping locally outward from the well in all of the layers of the storage region before buoyancy forces predominate and drive carbon dioxide upward where it will spread laterally under lower-permeability layers. Example simulations of carbon dioxide injection into a layered storage system with and without local flow control are presented to show the advantage of uniformly filling all layers and how RSE can be used to quantify storage efficiency for the two different injection approaches. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

03 NATURAL GAS↗

DOE OSTI2024

Putting the soil health principles to the test in Iowa

One of the most popular soil conservation campaigns is based on the USDA Natural Resource Conservation Service's Soil Health Principles (NRCS-SHPs). The NRCS-SHP program identifies four principles—maximize presence of living roots, minimize disturbance, maximize soil cover, and maximize biodiversity—with the underlying assumption that the more principles one follows, the greater improvements in soil health. Despite the popularity of the NRCS-SHPs, this underlying assumption has not been rigorously tested. To do so, we used nine long-term experiments all located in central Iowa, but with varying degree of NRCS-SHP adoption, to determine if greater adoption increases three slow-changing (maximum water holding capacity, bulk density [BD], and soil organic carbon) and three dynamic (microbial biomass carbon [MBC], potentially mineralizable carbon [PMC], and permanganate oxidizable carbon [POXC]) soil health indicators. We regressed these indicators with a soil health principle score that can scale soil management based on adoption of the NRCS-SHPs. Of the slow-changing soil properties, increased adoption of NRCS-SHPs only decreased soil BD (R2 = 0.22, p = 0.024). On the other hand, increased adoption of NRCS-SHPs strongly predicted increases in both MBC and PMC and across two sampling dates (R2 > 0.23, p < 0.015); POXC, however, did not increase with greater adoption. The consistent increases in MBC and PMC with greater adoption of NRCS-SHPs supports their usefulness as sensitive indicators of positive soil health change. Our study provides scientific evidence to support the NRCS-SHPs concept, improving its usefulness as an extension campaign, and stands as a step toward evidence-based soil conservation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Perfecting one-loop BCJ numerators in SYM and supergravity

We take a major step towards computing D-dimensional one-loop amplitudes in general gauge theories, compatible with the principles of unitarity and the color-kinematics duality. For n-point amplitudes with either supersymmetry multiplets or generic non-supersymmetric matter in the loop, simple all-multiplicity expressions are obtained for the maximal cuts of kinematic numerators of n-gon diagrams. At n = 6, 7 points with maximal supersymmetry, we extend the cubic-diagram numerators to encode all contact terms, and thus solve the long-standing problem of simultaneously realizing the following properties: color-kinematics duality, manifest locality, optimal power counting of loop momenta, quadratic rather than linearized Feynman propagators, compatibility with double copy as well as all graph symmetries. Color-kinematics dual representations with similar properties are presented in the half-maximally supersymmetric case at n = 4, 5 points. The resulting gauge-theory integrands and their supergravity counterparts obtained from the double copy are checked to reproduce the expected ultraviolet divergences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Analyzing the post-contrast attenuation of the esophageal wall on routine contrast-enhanced MDCT examination can improve the diagnostic accuracy in response evaluation of the squamous cell esophageal carcinoma to neoadjuvant chemoradiotherapy in comparison with the esophageal wall thickness

Purpose: To evaluate the accuracy of the multidetector computed tomography (MDCT) in the response evaluation of the esophageal squamous cell carcinoma (ESCC) to neoadjuvant chemoradiotherapy (nCRT) by analyzing the thickness and post-contrast attenuation of the esophageal wall after the nCRT. Methods: Contrast-enhanced (CE)-MDCT examinations in portal venous phase of one hundred patients with locally advanced ESCC who received nCRT and underwent esophageal resection and histopathology assessment of tumor regression grade (TRG) were retrospectively analyzed by measuring the maximal thickness and mean density of the esophageal wall in the segment involved by tumor and visually searching for hyperdense foci within it. Diagnostic performance was evaluated using the ROC analysis. Results: Average attenuation of the esophageal wall had stronger diagnostic performance for predicting pathologic complete regression (pCR) (AUC = 0.994; p < 0.001) in relation to maximal esophageal wall thickness (AUC = 0.731; p < 0.001). Maximal esophageal wall thickness ≤ 9 mm and average attenuation of the esophageal wall ≤ 64 HU predicted pCR with the sensitivity, specificity, and overall accuracy of 62.5%, 77.9%, and 73%, and 96.9%, 98.5%, and 98%, respectively. Combination of both cutoff values enabled correct assessment of pCR with the 100% accuracy. Visual detection of the hyperdense focus within the esophageal wall predicted pCR with the sensitivity, specificity, and overall accuracy values of 100%, 94.1%, and 96%, respectively. Conclusion: Visual analysis and measurement of post-contrast attenuation of the esophageal wall after the nCRT can improve diagnostic accuracy of MDCT in the response evaluation of the ESCC to nCRT in comparison with measuring the esophageal wall thickness.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Real-Time Analysis and Signal Optimization for Charge Detection Mass Spectrometry

Charge detection mass spectrometry (CDMS) is an important tool for measuring mass distributions for high mass samples and heterogeneous mixtures. In CDMS, single ions are trapped and their m/z and charge are measured simultaneously. As a single particle technique, the average signal must be optimized to maximize the number of single ion trapping events. If the average signal is too small, most of the trapping events will be empty, and if the average signal is too large, most of the trapping events will contain multiple ions. In recent embodiments, the time domain signal from the trapped ion is analyzed by fast Fourier transforms. The analysis time is much longer that the data collection time which precludes real-time optimization of the experimental conditions. In this paper, we describe the implementation of CDMS with real-time analysis. Processing the data in real time allows the average signal intensities to be dynamically optimized to maximize the number of single ion trapping events. Real-time analysis also allows the experimental settings to be optimized in a timely manner to target specific mass regimes to maximize the useful information content of the measurements. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Preliminary analysis of market potential for a hydropneumatic ground-level integrated diverse energy storage system

With the increasing share of renewable energy in the power grid, energy storage is considered a viable and effective solution to mitigate intermittency and improve power system stability. To quantify and estimate market potential for a recently developed Ground-Level Integrated Diverse Energy Storage (GLIDES) technology, the first operation model is developed for this hydropneumatic system and the bilinear pressure-volume terms are then linearized using the binary McCormick Relaxation method to improve solving efficiency. Based on up-to-date 5-min price data in four different real-time markets, several sets of experiments have been designed with three objective scenarios: maximize arbitrage, maximize arbitrage + reserve, maximize arbitrage + reserve + regulation. The experimental results show that the monetary value of GLIDES could be boosted by participation in ancillary markets and that regulation service might contribute more than half of the total revenue, even as high as 90% in PJM and ISONE markets. In addition, a case study for 1-day operation is used to illustrate the volume control of the GLIDES system.

25 ENERGY STORAGE↗

DOE OSTI2022

Economic analysis of CCUS: Accelerated development for CO 2 EOR and storage in residual oil zones under the context of 45Q tax credit

Residual oil zones (ROZ) undergoing CO 2 Enhanced Oil Recovery (CO 2 -EOR) may benefit from specific strategies to maximize their value. We evaluated several strategies for producing from a Permian Basin, West Texas, USA field’s ROZ. This ROZ lies below the main pay zone (MPZ) of the field. Such brownfield ROZs occur in the Permian Basin and elsewhere. Since brownfield ROZs are hydraulically connected to the MPZs, development sequences and schemes influence oil production, CO 2 storage, and net present value (NPV). We conducted economic assessments of various CO 2 injection/production schemes in the stacked ROZ-MPZ reservoir based on flow simulations of a high-resolution geocellular model built from wireline logs and core data and calibrated through production history matching. Flow simulations of water alternating gas (WAG) injection, such as switching injection from the MPZ to the ROZ and commingled production, were studied. Simulation results showed that simultaneous CO 2 injection into the MPZ and ROZ lead to the largest oil production and, generally, the largest NPV. If instead, CO 2 was simultaneously injected into the MPZ and ROZ, then into the ROZ alone, this maximized CO 2 storage. CO 2 storage can be used as a tax credit under the Internal Revenue Code, Section 45Q. Storage performance depends on the development approach and WAG ratio. Developing the ROZ increased storage compared to only producing from the MPZ. The WAG ratio to maximize oil production did not always yield the largest NPV. These findings are potentially applied to other Brownfield ROZs, which are common below San Andres reservoirs in the Permian Basin and other basins. ROZ development can increase oilfields’ NPV and carbon storage potential. Our study can serve as an analog for similar reservoirs. Here this work provides valuable insights into the further optimization of brownfield ROZ development and information for operators to plan to develop stacked ROZ-MPZ reservoirs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗

DOE OSTI2024

Hierarchically porous electrospun carbon nanofiber for high-rate capacitive deionization electrodes

Capacitive deionization (CDI) is a promising technology that has gained interest for the desalination of brackish water. Hierarchically porous carbons are commonly used as electrodes for CDI due to their high surface areas and controlled pore size distributions that maximize ion adsorption capacity and rate. Electrospinning is an effective way of generating carbon nanofibers with high inter-fiber macroporosity that can be further modified to improve surface area, total pore volume, and pore size distribution. This work describes the use of sacrificial mesopore formers in tandem with a micropore etching technique to induce hierarchical porosity in electrospun fibers. Mesopores are formed via the dissolution of silica nanoparticles that are introduced into the fibers during the electrospinning step. After mesopore formation, micropores are etched into the resulting surface through KOH impregnation and thermal activation. This sequential technique creates a hierarchical network of pores from the inherent macroporosity of the fiber network, to the mesopores, and finally micropores to simultaneously maximize surface area and accessibility. Micropore formation is optimized to maximize specific surface area while maintaining physical integrity of the fibers. Further, the combination of mesopores and micropores enables fast ion adsorption rates and capacity. Carbon fiber electrodes fabricated in this method achieve specific surface areas exceeding 1400 m 2 g -1 , with pore volumes exceeding 1.0cc g -1 . The pore size distributions are highly controlled, with 80% of total pore volume coming from pores <20nm in radius. In 500 ppm constant voltage CDI tests, these fiber electrodes obtain a salt adsorption capacity of over 14 mg g -1 at a salt adsorption rate of ~4mg g -1 min -1 , showcasing the high capacity matched with high rate of these easily fabricated, inexpensive materials.

36 MATERIALS SCIENCE↗

DOE OSTI2018

ULAMA: A Utilization-Aware Matching Approach for robust on-demand spatial service brokers

This article investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding time of service to mobile consumers while meeting the consumer’s maximum travel time and wait time constraints. The goal of the broker is to maximize the number of matched requests while also keeping the “eco-system” functioning and robust against variations in the supply–demand ratio by engaging as many service providers as possible and maintaining providers’ participation to ensure that providers do not drop out. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g., on-demand ride-hailing services, on-demand food delivery, etc.). Challenges of this problem include the need to satisfy many conflicting requirements of the broker, consumers and service providers and the high computational complexity since solving the problem for a number of available consumers at any time instant is NP-hard. Related work in spatial crowdsourcing and ridesharing has mainly focused on maximizing the number of matched requests and minimizing travel cost, but did not consider the importance of maintaining provider engagement and balancing provider utilization, which could become a priority when the available supply exceeds the demand. In this paper, we propose a Utilization-Aware Matching Approach (ULAMA) which employs novel provider-centric heuristics for balancing the utilization of providers, and a consumer-priority-based greedy matching algorithm that prioritizes consumers for maximizing the number of matched requests. Experimental results show that our proposed approach outperforms related work by achieving the lowest variance in provider utilization while matching all available providers even when supply greatly exceeds demand. Our approach also achieved a larger number of matched requests, particularly when supply exceeds demand and also when both supply and demand are balanced.

97 MATHEMATICS AND COMPUTING↗