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All sources NASA NTRS DOE OSTI

DOE OSTI2013

Macromolecule Sorption and Diffusion in HEMA/MAA Hydrogels

Transient solute absorption and desorption concentration profiles were measured in a 70 wt % hydroxyethyl methacrylate (HEMA)/30 wt % methacrylic acid (MAA) anionic hydrogel using two-photon confocal microscopy. Dilute aqueous solutes included fluorescently labeled dextrans with molecular masses of 4, 10, and 20 kDa, and fluorescently labeled cationic avidin protein. Cross-linking densities with ethylene glycol dimethacrylate (EGDMA) varied from 0 to 1 wt % with polymer volume fractions increasing from 0.15 to 0.25. Average gel mesh sizes, determined from zero-frequency oscillatory shear storage moduli, ranged from about 3.6 to 8.4 nm over the cross-link ratios studied. All solutes exhibit Stokes-Einstein hydrodynamic radii obtained from measured free diffusion coefficients, D o , comparable to or larger than the average gel mesh size. In spite of considerable size exclusion, the studied solutes penetrate the gels indicating a range of mesh sizes available for transport. Transient uptake and release concentration profiles for FITC-dextrans follow simple diffusion theory with diffusion coefficients, D , essentially independent of loading or release characteristic of reversible absorption. Although strongly size-excluded, these solutes do not interact specifically with the polymer network. Diffusivities are accordingly predicted from a large-pore effective-medium (LPEM) model developed to account for solute size, hydrodynamic drag, and distribution of mesh sizes available for transport in the polymer network. For this class of solute, anrs of magnitude slower than that of dextran of similar hydrodynamic radius. Desorption of avidin is not cd using no adjustable parameters, diffusivities predicted from the new effective-medium model demonstrate good agreement with experiment. For the specific-interacting cationic protein, avidin, gel loading is 3 ordeomplete even after 2 weeks of extraction. On the basis of size alone, avidin is strongly size-excluded, yet it exhibits a partition coefficient of over 20. For the positively charged protein, we observed specific ion binding on the negatively charged carboxylate groups of MAA-decorated polymer strands in the larger mesh spaces. Finally, simple linear sorption kinetics gives an adsorption time constant of 5 min and a desorption time constant of about 20 days, suggesting nearly irreversible uptake of cationic avidin on the anionic gel matrix.

diffusion↗

DOE OSTI2026

Charge Density Mismatch is a Key Characteristic of Highly Concentrated Electrolyte Solutions and Highly Water‐Soluble Salts

Only very soluble electrolytes can form concentrated solutions. Some salts are so soluble that there are less than four water molecules per ion in saturated solution. Ions usually form clusters or networks with more than one counterion in their coordination sphere in these concentrated solutions. Do these ultraconcentrated solutions form because the counterions have high affinity for each other in liquid, or because they have a poor affinity for each other in solids? Here, in this study, this question is addressed using the valence matching principle of the bond valence model by comparing the charge density mismatch between counterions to their solubility for a series of alkali fluorides, carboxylates, and oxyanions. The solubilities are plotted against the characteristic average bond valence of the alkali, and the lowest solubilities are those where alkali and anion had matching bond valences. Conversely, the highest solubilities are those with poorly matching bond valences. Available ion-pairing constants indicate that the weakest ion-pairs are those with the largest bond valence mismatch, indicating that the large water solubilities occur despite weak ion-pairing rather than because of strong ion-pairing. Therefore, a key characteristic of highly water-soluble salts is that the counterions have mismatched charge densities.

concentrated solutions↗

NASA NTRS1999

A Neural Network Aero Design System for Advanced Turbo-Engines

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Sanz, Jose M.↗

DOE OSTI2021

Challenges and Solutions for Real-Time Phasor Modeling of Large-scale Distribution Network with High PV Penetration

The conversion process of a practical large-scale feeder data from a quasi-static time series (OpenDSS) model into a real-time phasor model (ePHASORSIM in Opal-RT) is discussed in the paper. The process is implemented using an open source Python software. Previous reported implementations for the conversion process lead to several errors when applied to a larger-scale system such as the one considered here. Hence in this work, we describe the common issues in this conversion and propose a customized solution to enhance the efficiency of the conversion and reduce the complexity in the process. A quantitative validation of the enhanced conversion process is presented in this work using an actual high PV penetration feeder model that consists of 2230 buses, and using actual load and PV profile data. After a detailed analysis, this customized conversion software will be made available as an open source tool and is expected to be helpful for researchers who want to pursue a similar conversion. Solutions to various observed issues such as identifying the lines due to islanded network, representation of full impedance model of transformer/lines as sequential models, complexity in the representation of single phase buses/lines as three phase buses/lines to make it compatible with the simulator platform are discussed. Comparison of power flow and time series simulation results obtained from both OpenDSS and ePHASORsim models show very low errors, validating the accuracy of the proposed conversion process.

14 SOLAR ENERGY↗

DOE OSTI2017

An Incentive-based Online Optimization Framework for Distribution Grids

This article formulates a time-varying social-welfare maximization problem for distribution grids with distributed energy resources (DERs) and develops online distributed algorithms to identify (and track) its solutions. In the considered setting, network operator and DER-owners pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. The proposed algorithm affords an online implementation to enable tracking of the solutions in the presence of time-varying operational conditions and changing optimization objectives. It involves a strategy where the network operator collects voltage measurements throughout the feeder to build incentive signals for the DER-owners in real time; DERs then adjust the generated/consumed powers in order to avoid the violation of the voltage constraints while maximizing given objectives. Stability of the proposed schemes is analytically established and numerically corroborated.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Role of the Reference Bus in Three-Phase State Estimation

As more and more renewable energy sources are being connected to the power grid at lower voltage levels, closer monitoring of distribution networks is becoming a necessity. This is accomplished by state estimation and deployment of Distribution Management Systems (DMS). Distribution feeders are typically untransposed with unbalance loads. Thus, their modeling and analysis show significant differences compared to those for transmission networks. This paper focuses on the state estimation problem and its formulation for unbalanced distribution systems. Specifically, it is concerned about the modeling and solution of three-phase networks without a fully balanced bus to be selected as the angle reference. It is shown that best results will be obtained via the use of a virtual symmetrical bus. Validation of the proposed formulation is carried out by simulations.

Langner, A. L.↗

DOE OSTI2023

Elastomeric vitrimers from designer polyhydroxyalkanoates with recyclability and biodegradability

Cross-linked elastomers are stretchable materials that typically are not recyclable or biodegradable. Medium-chain-length polyhydroxyalkanoates (mcl-PHAs) are soft and ductile, making these bio-based polymers good candidates for biodegradable elastomers. Elasticity is commonly imparted by a cross-linked network structure, and covalent adaptable networks have emerged as a solution to prepare recyclable thermosets via triggered rearrangement of dynamic covalent bonds. Here, we develop biodegradable and recyclable elastomers by chemically installing the covalent adaptable network within biologically produced mcl-PHAs. Specifically, an engineered strain of Pseudomonas putida was used to produce mcl-PHAs containing pendent terminal alkenes as chemical handles for postfunctionalization. Thiol-ene chemistry was used to incorporate boronic ester (BE) cross-links, resulting in PHA-based vitrimers. mcl-PHAs cross-linked with BE at low density (<6 mole %) affords a soft, elastomeric material that demonstrates thermal reprocessability, biodegradability, and denetworking at end of life. The mechanical properties show potential for applications including adhesives and soft, biodegradable robotics and electronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Designing networks: A mixed-integer linear optimization approach

Designing networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes, and the analysis of network temporal evolution. Despite the importance of the task, there currently exists a gap in our ability to systematically generate networks that adhere to theoretical guarantees for the given property specifications. In this work, we propose the use of Mixed-Integer Linear Optimization modeling and solution methodologies to address this Network Generation Problem. We present useful modeling techniques and apply them to mathematically express and constrain a broad class of network properties in the context of an optimization formulation. We derive complete formulations for the generation of networks that attain specified levels of connectivity, spread, assortativity and robustness, and we illustrate these via a number of computational case studies.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Development of programmable artificial neural networks

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Meade, Andrew J.↗

NASA NTRS1997

Parallel Computation of Unsteady Flows on a Network of Workstations

Parallel computation of unsteady flows requires significant computational resources. The utilization of a network of workstations seems an efficient solution to the problem where large problems can be treated at a reasonable cost. This approach requires the solution of several problems: 1) the partitioning and distribution of the problem over a network of workstation, 2) efficient communication tools, 3) managing the system efficiently for a given problem. Of course, there is the question of the efficiency of any given numerical algorithm to such a computing system. NPARC code was chosen as a sample for the application. For the explicit version of the NPARC code both two- and three-dimensional problems were studied. Again both steady and unsteady problems were investigated. The issues studied as a part of the research program were: 1) how to distribute the data between the workstations, 2) how to compute and how to communicate at each node efficiently, 3) how to balance the load distribution. In the following, a summary of these activities is presented. Details of the work have been presented and published as referenced.

Source record↗

NASA NTRS1991

Space Network Control Conference on Resource Allocation Concepts and Approaches

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

Moe, Karen L.↗

DOE OSTI2018

Effect of Poly(ethylene glycol) Grafting Density on Methylcellulose Fibril Formation

Herein we investigate the effect of short-chain poly(ethylene glycol) (PEG) graft density on the formation of methylcellulose (MC) fibrils at elevated temperatures. Thiol–ene click chemistry was used to systematically graft 800 and 2000 g/mol PEG onto the backbone of allylated MC, with a wide range of grafting densities from 0.7% to 33%. As determined from light scattering, grafting leads to an increase in the persistence length of this semiflexible copolymer, by as much as a factor of 10. Upon heating, SAXS and AFM studies show that fibril formation is suppressed at around 10% grafting density for shorter PEG grafts, corresponding to persistence lengths about ~22 nm. For longer grafts fibril formation is suppressed at 7% grafting density, at around the same ~22 nm persistence length. The radius of the fibrils increases with the square root of the persistence length of the chains, which is consistent with a theory for the radius of twisted chains. The ability to form networks at 80 °C is highly correlated to the ability to form fibrils, and accordingly the modulus systematically decreases with grafting density. When the fibril formation is suppressed, MC solutions no longer form solid networks. Therefore, grafting modulates the molecular architecture and gelation properties of MC and also provides new insight into the structure of MC fibrils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1995

Reducing neural network training time with parallel processing

Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

Rogers, James L., Jr.↗

NASA NTRS2009

802.16e System Profile for NASA Extra-Vehicular Activities

This report identifies an 802.16e system profile that is applicable to a lunar surface wireless network, and specifically for meeting extra-vehicular activity (EVA) data flow requirements. EVA suit communication needs are addressed. Design-driving operational scenarios are considered. These scenarios are then used to identify a configuration of the 802.16e system (system profile) that meets EVA requirements, but also aim to make the radio realizable within EVA constraints. Limitations of this system configuration are highlighted. An overview and development status is presented by Toyon Research Corporation concerning the development of an 802.16e compatible modem under NASA s Small Business Innovative Research (SBIR) Program. This modem is based on the recommended system profile developed as part of this report. Last, a path forward is outlined that presents an evolvable solution for the EVA radio system and lunar surface radio networks. This solution is based on a custom link layer, and 802.16e compliant physical layer compliant to the identified system profile, and a later progression to a fully interoperable 802.16e system.

Foore, Lawrence R.↗

DOE OSTI2015

Intermolecular Network Theory:A General Approach for Understanding the Structural and Dynamic Properties of Liquids and Solutions

Graph theory applications within the physical sciences have a long history. However, it has never moved to the forefront of analytical techniques employed by the computational chemistry community. Though much of the earliest work leveraged percolation theory as a backdrop for understanding critical phenomena and phase transitions within statistical mechanical simulations, modern data science has much to contribute. Here in this paper, we focus on the realm of intermolecular networks, where vertices represent molecules or particles, and an edge represents an intermolecular or interparticle interaction. These interactions are widely understood to dictate many of the physical properties of soft matter, and for liquids in particular, understanding this network is essential. Consider the sheer volume of ~70 years of literature associated with the hydrogen bond network of water, a topic that remains an active area of research even today. The aim of this work is twofold: first, to put into context prior algorithms and analyses that employed “connectedness theory” or lattice-based models to understand the intermolecular networks of chemical systems; and second to discuss a more general strategy for analyzing the multiscale structural and dynamic properties of chemical systems—intermolecular network theory. This approach encompasses foundational methods such as percolation theory, but also utilizes contemporary graph theoretical analyses that have evolved alongside the development of the World Wide Web, cloud computing, and big data. It is the realm of intermolecular network theory to determine the relationships between the essential physics of a system and the topological properties of the network and to derive new techniques in graph theory that can be related to the underlying physico-chemical or reactive properties of a system. Examples are used throughout this chapter that demonstrate applications to a wide variety of complex chemical systems, including ion association within electrolytes, mass transport and the properties of liquid–liquid phase boundaries, and multicomponent solutions.

Complex networks↗

DOE OSTI2020

In-Use Interfacial Stability of Monoclonal Antibody Formulations Diluted in Saline i.v. Bags

The use of monoclonal antibodies (mAbs) for the treatment of a variety of diseases is rapidly growing each year. Many mAbs are administered intravenously using i.v. bags containing 0.9% NaCl (normal saline). We studied the aggregation propensity of these antibody solutions in saline and compared it with a low ionic strength formulation buffer. Furthermore, the mAb studied in this work is prone to aggregate, and is known to form a viscoelastic network at the air-solution interface. We observed that this interfacial elasticity increased when formulated in saline. In the bulk, the mAbs exhibited a tendency to self-associate that was higher in saline. We also studied the aggregation of the mAbs in the presence of polysorbate-20, typically added to formulations to mitigate interfacial aggregation. We observed that with surfactants, the presence of salt in the buffer led to a greater mAb adsorption at the interface and resulted in the formation of more particulate aggregates. Our results show that the addition of salt to the buffer led to differences in the interfacial aggregation in mAb formulations, showing that stress studies used to screen for mAb aggregation intended for i.v. administration should be performed in conditions representative of their intended route of administration.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Ensuring Solution Uniqueness in Three-Phase Power System State Estimation

This paper is concerned with the issue of potential non-unique solutions in three-phase state estimation. Theory of observability analysis for positive sequence power system state estimation is based on certain assumptions that avoid possibility of multiple solutions. Also, it is shown that observability of a positive sequence network remains independent of the network parameters or the operating state. When extending single-phase observability analysis directly to the three-phase case, this paper considers the possibility of converging to multiple solutions, i.e. solution non-uniqueness, even for cases where state estimator successfully converges. The study illustrates via numerical examples the likelihood of converging to entirely different solutions for certain network parameters. It also examines how the operating state, particularly under unbalanced loading, leads to solution non-uniqueness. The paper then describes an alternative approach to ensure a unique solution in three-phase state estimation. This method aims to accurately and uniquely estimate the state of any unbalanced three-phase system, irrespective of load imbalance, network configuration, existence of synchronous generators or transformers.

Power System State Estimation, Three-Phase, Distri↗

DOE OSTI2021

Resonant amplification of enzymatic chemical oscillations by oscillating flow

Using theory and simulation, we analyzed the resonant amplification of chemical oscillations that occur due to externally imposed oscillatory fluid flows. The chemical reactions are promoted by two enzyme-coated patches located sequentially on the inner surface of a pipe that transports the enclosed chemical solution. In the case of diffusion-limited systems, the period of oscillations in chemical reaction networks is determined by the rate of the chemical transport, which is diffusive in nature and, therefore, can be effectively accelerated by the imposed fluid flows. In this work, we first identify the natural frequencies of the chemical oscillations in the unperturbed reaction–diffusion system and, then, use the frequencies as a forcing input to drive the system to resonance. We demonstrate that flow-induced resonance can be used to amplify the amplitude of the chemical oscillations and to synchronize their frequency to the external forcing. In particular, we show that even 10% perturbations in the flow velocities can double the amplitude of the resulting chemical oscillations. Particularly, effective control can be achieved for the two-step chemical reactions where during the first half-period, the fluid flow accelerates the chemical flux toward the second catalytic patch, while during the second half-period, the flow amplifies the flux to the first patch. The results can provide design rules for regulating the dynamics of coupled reaction–diffusion processes and can facilitate the development of chemical reaction networks that act as chemical clocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗