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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-Time Distributed Control of Smart Inverters for Network-level Optimization

The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.

voltage control, optimization, reactive power, inv↗
DOE OSTI2022

Predicting plant attractiveness to coccinellids with plant trait profiling, citizen science, and common garden surveys

Lady beetles are key predators in agricultural systems and their impact can be enhanced by plant resources. Facilitation of lady beetles is a potentially effective pest management strategy but a lack of data on which plant species attract and nurture them limits implementation. Here we investigate which plant species might optimize lady beetle impact by developing a profile of plant characteristics most likely to attract lady beetles and testing if the subset of “potentially preferred” plant families exhibiting those characteristics are significantly more attractive to lady beetles than other families. This testing encompassed a combination of observations submitted to a citizen science program, the Lost Ladybug Project, and two repeated site surveys. Traits reported to be most attractive to lady beetles included the presence of trichomes that provide protection from predation, nutritional resources such as nectar or prey, and visual or chemical cues associated with those traits. Significantly more lady beetles were observed on the three plant families which most often exhibit these traits, Apiaceae, Asteraceae, and Rosaceae, than on other families. Furthermore, strategies for incorporating plants from these families into production systems to attract lady beetles and limit pest damage are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A fitting algorithm for optimizing ion implantation energies and fluences

In this work we describe a method to automatically generate an ion implantation recipe, a set of energies and fluences, to produce a desired defect density profile in a solid using the fewest required energies. We simulate defect density profiles for a range of ion energies, fit them with an appropriate function, and interpolate to yield defect density profiles at arbitrary ion energies. Given Ν energies, we then optimize a set of Ν energy-fluence pairs to match a given target defect density profile. Finally, we find the minimum Ν such that the error between the target defect density profile and the defect density profile generated by the Ν energy-fluence pairs is less than a given threshold. Inspired by quantum sensing applications with nitrogen-vacancy centers in diamond, we apply our technique to calculate optimal ion implantation recipes to create uniform-density 1 μm surface layers of 15 N or vacancies (using 4 He).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage

This study investigates the technical and economic feasibility of using high levels of solar energy penetration up to 400 MW into a smart grid system of 60,000 smart houses. A novel non-cooperative Stackelberg game is introduced that incorporates the profitability of the supply-side and helps in solving problems related to overgeneration and photovoltaic curtailment. The non-cooperative game is intended to find the optimal dynamic prices that would leverage distributed storage through the demand-side to stabilize the power grid operation. Ten cases are studied with five photovoltaic plant sizes and two battery designs. Here, a novel quantitative analysis of high levels of solar penetration as a percentage of the total electricity demand is introduced to evaluate the technical feasibility of the studied cases. To evaluate the economic viability of the proposed smart grid system, four metrics were used: the levelized cost of energy, the levelized cost of storage, the payback period, and the net present value. Two out of ten studied cases were concluded to be the most promising cases, one with a solar photovoltaic plant size of 200 MW and the other with 300 MW. The case with 300 MW solar plant is preferred as it paves the way for more solar energy deployment with a solar penetration percentage up to 67.78%. This case had a payback period of 10.72 years and a net present value of $\$51.44$ M for the solar plant and a payback period of 12.06 years and a net present value of $\$40.75$ M for the demand-side.

14 SOLAR ENERGY↗
DOE OSTI2022

Rare earth element separations by high-speed counter-current chromatography

We report that following the initial development of High-Speed Counter-Current Chromatography (HSCCC) in the 1960s, several studies have explored its applicability in the separation of rare earth elements (REEs). More recently, however, HSCCC publications have transitioned towards the separation of natural products or pharmaceuticals, leaving the application for REEs largely unexplored from a practical standpoint. Herein, we expand upon prior work in this field by evaluating the suitability of HSCCC to separation of a subset of non-radioactive REEs (Nd, Sm, Eu, Tb, and Y) at 10 -4 mol levels using di-(2-ethylhexyl)phosphoric acid (HDEHP) in n-heptane as the stationary phase and hydrochloric acid as the mobile phase. First, the effect of flow rate on the stationary phase volume retention ratio and resolution of Nd/Sm/Eu subgroup was evaluated followed by optimization of step-gradient elution profiles resulting in additional recovery of Tb and Y within a seven-hour window. The five REEs were separated at the baseline resolution level or above. Elution profiles obtained from multiple runs across two independently operated columns and across independent runs were cross analyzed. Reproducibility in elution profiles point to future applications in radioelement separation chemistry, where both chemical and radiochemical purity are of importance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2024

Near wake evolution of a tidal stream turbine due to asymmetric sheared turbulent inflow with different integral length scales

Tidal stream turbines deployed at highly energetic open water sites are subjected to sheared inflow in the rotor plane. The inflow shear is expected to cause asymmetric loading on the rotor blades and affect the downstream wake. In the current study, two different turbulent inflow conditions, static-high shear and dynamic shear, were generated via an active-grid turbulence generator. A 1:20 scaled three-bladed horizontal axis tidal turbine model was tested in those conditions. The results were compared to a quasi-laminar case with no imposed turbulence or shear. The results show that the high shear reduces the average performance, with a drop of up to 16% in the optimal power coefficient. Besides, the shear profiles increase torque fluctuations and induce significant differences in wake hydrodynamics between the high-speed (upper) and low-speed (lower) regions. The large integral length scales further enhance the load fluctuations perceived by the rotor but have a negligible effect on the mean wake field quantities and the wake recovery. The lower half region featured a faster breakdown of tip vortex structure and a rapid drop of swirl number, a phenomenon conjectured to be a consequence of the strong turbulence intensities and Reynolds stresses in the lower half region. Furthermore, the sheared turbulent inflow also results in a very intensive energy redistribution process towards large-scale, low-frequency motions, which is important to the downstream turbines.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

System benefits of industrial battery storage: A comparison of grid and facility control and dispatch

Customer-owned, distributed battery installations are being incentivized by utilities to increase installed battery capacity. In many of these incentive agreements, the battery owner relinquishes battery control to the utility in exchange for incentive money. The industrial sector has lagged in storage installation when compared to the residential and commercial sectors. This study compares the economic advantages to utilities and industrial facilities in different dispatch control situations. The study presents a novel framework for the optimization of multiple systems using load profiles from the industrial, residential, and commercial sectors. Case studies are presented to illustrate different dispatch scenarios. Further, the simulations showed more fiscal benefit for the industrial facilities to dispatch the battery for electrical demand reduction than utility dispatch. In the case studies, facility dispatch control resulted in an increase of facility savings by a factor of about 8.7 when compared to utility dispatch. Battery size plays a significant factor on the impact of the grid’s generating costs, showing that larger batteries can provide significant benefit even if dispatched by the facility. Future policies concerning industrial battery installations should consider overall economic benefits to utility and facility in the form of rate structures and incentive participation based on battery size.

25 ENERGY STORAGE↗
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
DOE OSTI2025

Interference fringe (IF) technique for droplet contact angle and thickness measurements: Ultralow contact angle and nanoscale thickness measurement

Recently, reflection interference fringe (RIF) and transmission fringe (TIF) techniques have been introduced to investigate the origin of far-field interference fringe (IF) formation and to determine a droplet's contact angle and thickness by measuring the fringe radius. In this study, characteristics of the IF technique are analyzed based on the RIF and TIF by varying the schematics, such as configuration (transmission/reflection), the droplet's side (left-hand side/right-hand side), and the substrate types (flat/prism). The analysis also investigates the refraction effect at the droplet edge and the maximum incidence and contact angles. The schematic variation shows that the widest contact angle range can be measured in a transmission configuration with droplet's right-hand side, and that the fringe radius decreases with incidence angles on a prism substrate, consistent with the recent observation. Refraction at the droplet edge causes the fringe radius to increase or decrease depending on the degree of refraction. Based on the characteristics study, it is revealed that the IF technique can determine nanometer-scale thicknesses below 100 nm on droplets, corresponding to ultra-small contact angles of less than 0.01°, with an extended working distance of 3000 mm and an optimized incidence angle, assuming a spherical profile. This finding is significant, as it demonstrates that the nanoscale thickness can be determined in situ under ambient conditions using a simple optical configuration, without requiring a sophisticated setup, such as a microscope. It is anticipated that the IF technique can be combined with other nanoscale thickness measurement techniques to enhance its measurement reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Janus v1.0

Janus provides a software framework for lightweight container management and orchestration. It's primary use cases are around deploying containerized services for high-performance data movement needs. Thus, Janus differentiates itself from systems like Kubernetes by tailoring the deployment of containers around network, storage, and host tuning optimizations. Janus uses the concept of profiles to capture repeatable deployment patterns and applies them to container execution across one or more endpoints. A Janus Agent component provides remote host tuning and monitoring capabilities.

Essiari, Abdelilah [Lawrence Berkeley National Lab↗
DOE OSTI2019

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

Background Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. Results We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (“inertia”) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation. Conclusions Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Mathematical & Computational Biology↗
DOE OSTI2021

Spatio-temporal Estimates of Disease Transmission Parameters for COVID-19 with a Fully-Coupled, County-Level Model of the United States

Sandia National Laboratories has developed a capability to estimate parameters of epidemiological models from case reporting data to support responses to the COVID-19 pandemic. A differentiating feature of this work is the ability to simultaneously estimate county-specific disease transmission parameters in a nation-wide model that considers mobility between counties. The approach is focused on estimating parameters in a stochastic SEIR model that considers mobility between model patches (i.e., counties) as well as additional infectious compartments. The inference engine developed by Sandia includes (1) reconstruction and (2) transmission parameter inference. Reconstruction involves estimating current population counts within each of the compartments in a modified SEIR model from reported case data. Reconstruction produces input for the inference formulations, and it provides initial conditions that can be used in other modeling and planning efforts. Inference involves the solution of a large-scale optimization problem to estimate the time profiles for the transmission parameters in each county. These provide quantification of changes in the transmission parameter over time (e.g., due to impact of intervention strategies). This capability has been implemented in a Python-based software package, epi_inference, that makes extensive use of Pyomo [5] and IPOPT [10] to formulate and solve the inference formulations.

42 ENGINEERING↗
DOE OSTI2025

Evaluating Variable-Impedance Magnetically-Insulated Transmission Lines as a Risk-Mitigation Measure for Next-Generation Pulsed Power

This project has produced the first detailed characterizations of power flow resulting from applying the “variable-impedance MITL” concept to real-life systems in Sandia’s pulsed power program (Z and next-generation pulsed power (NGPP)). We present simulation results and analyses for constant-impedance versions of both Z and NGPP and survey the operational viability of several variable-impedance re-designs in the parameter space of linear tapers. Circuit modeling (SCREAMER/Bertha) was used to pinpoint promising candidate designs, and EM-PIC (Empire) simulations were used to evaluate these candidates more rigorously. This approach was particularly successful in the Z regime which resulted in the identification of several viable variable-impedance MITL designs for each level. The approach was more challenged in the operating space NGPP occupies, producing data points that speak to a more restrictive design space due to anode plasma turn-on. In the end, we were able to converge on one viable variable-impedance design for the highest inductance line (level “F”) and one for the highest current line (level “A”). Altogether, the body of simulation evidence presented in this report suggest there does exist flexibility in operating space for magnetically-insulated transmission lines (MITLs) having variable geometric impedance to be a potential enabling technology for safely increasing current delivery (and potentially lowering stack voltage) in pulsed-power drivers by manipulating electron losses; however, operating points for a particular design must be carefully screened. Circuit and EM-PIC modeling provided consistent verdicts in safe operating regimes for operational viability, but additional physics such as anode plasma turn-on which is included in Empire but not in SCREAMER/Bertha was found to be a critical factor affecting power flow that lead to different assessments between the codes. It is not always the case that the occurrence of anode plasma caused a design to fail (some designs turned on anode plasma yet still delivered load currents meeting design targets); the details matter such as how early in the pulse anode surfaces break down (and how large a region). However, in every case that it did fail it was found that the feedback from anode plasma was the cause (i.e., turning off the anode plasma model in Empire restored agreement with the circuit model prediction). As circuit simulations represent an efficient and practical means of surveying design space compared to more computationally-expensive approaches such as EM-PIC, it could be prudent to invest in the research and development of models to include the effects of anode plasma such as ion emission in circuit codes. The variable-impedance MITL design is a new concept that enables controlled manipulation of the initial electron losses in the outer MITL and can be tested on Z today. We encourage follow-on work to explore further optimization (including alternative variable-impedance profiles, e.g., having constant dZ/dR), and to confirm the major findings presented in this report by fielding test hardware on actual Z shots.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

VALVE TIMING AND AREA PROFILE SELECTION FOR HYDRAULIC PUMPS AND MOTORS

A significant amount of research has been conducted to select valve timing and area profiles that create efficient and quiet hydraulic pumps and motors. Numerous active valve architectures have been modeled and optimized, but the rationale for the final solution is often unclear. The solution is usually highly dependent on the modeled valve geometry constraints and the duty cycle of the pump or motor for which the valve was optimized. This paper first presents an ideal valve area profile and what compromises are made to design for a working pressure and speed range. The ideal valve area profile is resolved using a piston-cylinder model with a pressure dependent bulk modulus and variable valve openings. The piston-cylinder is first modeled as a pump with passive (check) valves and a specified pressure drop across the valves to determine the ideal area profile. A generalization of the ideal timing is demonstrated for positive pressure differential and positive rotation direction, also known as the first quadrant. The effects on valve timing when varying pressure are shown for the first quadrant, but the trends can be extrapolated for all quadrants of operation. The ideal valve profile is implemented as fixed valve timing, meaning the valve area is only a function of the timing angle, in a pump-motor. The pump-motor is simulated in one rotation direction through a pressure range. Performance is high in pumping operation, but when the pressure differential is reversed, cylinder pressure spikes ensue. Two strategies to modify an ideal valve area profile are presented: timing grooves and a pressure shifted valve timing. Timing grooves reduce pressure spikes and cavitation in the cylinder but generally increase throttling losses. A pressure shifted valve timing has lower throttling energy losses, making it the favored solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characterization of flowing liquid films as a regenerating plasma mirror for high repetition-rate laser contrast enhancement

In this paper, we characterize a high repetition-rate regenerating plasma mirror produced by the thin film of liquid formed when two laminar streams collide. The use of a flowing liquid film is inexpensive and the interaction surface refreshes automatically, avoiding buildup of on-target debris. The composition of the liquid material and the relative angle of the film-generating nozzles was optimized for this application. Spectra measured in reflection from a water-based plasma mirror showed a blue shift but an optical reflectivity of up to 30%. The thickness of the film was found to be of the order of 2 μm, and the stability of the reflected spot was ≈1 mrad. The reflected beam profile was highly distorted but stable. Further optimization of the nozzles to affect the fluid flow should enable significant improvements in control of the fluid films and increase in the reflectivity of these mirrors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Optimal sensor network design for multi-scale, time-varying differential algebraic equation systems: Application to an entrained-flow gasifier refractory brick

An algorithm for optimal sensor network design for multi-scale, time-varying differential algebraic equation systems with non-separable dynamics is presented. As the process is time-varying, an integral normalized posterior error covariance of a multi-scale filter is minimized to obtain the optimal sensor locations. For reducing the computational cost, an adaptive sampling rate approach is considered for the slowly-varying variables. The algorithm is applied to a smart refractory brick with embedded sensors as part of an entrained-flow gasifier. Thermistors and interdigital capacitors are considered as candidate measurement technologies for estimating temperature and slag penetration profile along the gasifier wall. Finally, when the optimal set of sensors obtained from the algorithm is used for estimating temperature and slag penetration profiles in a multi-scale Kalman filter framework, satisfactory estimates are obtained despite high measurement noise and model mismatch.

42 ENGINEERING↗