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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control

Here, we study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm exhibits local linear convergence, and that the convergence rate improves exponentially with the overlap size. We also establish global convergence results for a general quadratic programming, which enables the application of the Schwarz scheme inside second-order optimization algorithms (e.g., sequential quadratic programming). The theoretical foundation of our convergence analysis is a sensitivity result of nonlinear OCPs, which we call "exponential decay of sensitivity" (EDS). Intuitively, EDS states that the impact of perturbations at domain boundaries (i.e., initial and terminal time) on the solution decays exponentially as one moves into the domain. Here, we expand a previous analysis available in the literature by showing that EDS holds for both primal and dual solutions of nonlinear OCPs, under uniform second-order sufficient condition, controllability condition, and boundedness condition. We conduct experiments with a quadrotor motion planning problem and a partial differential equations (PDE) control problem to validate our theory, and show that the approach is significantly more efficient than alternating direction method of multipliers and as efficient as the centralized interior-point solver.

42 ENGINEERING↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
DOE OSTI2025

Thermal Response of a Lithium Vapor Divertor to Cyclical Operation

The lithium vapor divertor concept is being developed as a method to achieve detached divertor conditions in a tokamak while minimizing impurity radiation losses from the core plasma. SOLPS-ITER modeling has previously been used to identify some of the geometric constraints and required lithium evaporation rate of a lithium vapor divertor in a medium-sized tokamak during steady-state operation. Here an updated conceptual design based on these operating requirements is introduced and the thermal response of the system is modeled during cyclical operation, consistent with operation in a short-pulse tokamak. Controllability of the temperature of the lithium capillary porous system (CPS) is achieved by adopting a design where there is no line-of-sight for radiation from the plasma to reach the heated CPS surface. Operational strategies to minimize the amount of lithium evaporated between plasma discharges while achieving steady evaporation rates during plasma discharges are discussed and modeled here. The optimal feedforward control strategy demonstrated in this work is to ramp up the temperature of the evaporator as quickly as possible immediately before a plasma discharge and then reduce the heating to match the desired steady-state net evaporation rate just before the plasma discharge begins, allowing the thermal inertia of the system to stabilize the evaporation rate during the first second of the plasma discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Macroscale property assessment and indentation characteristics of thick section friction stir welded AA 5083

The spatial gradients in microstructure have been proven to be effective in optimizing the performance of components, and several techniques have been developed to achieve this microstructure. Nevertheless, the local microstructural modification methods inevitably result in the formation of gradient microstructure leading to a variation in the property. Herein this work systematically investigates the effect of gradient microstructure formed during thick section friction stir weld Al5083 alloy on its mechanical properties. A novel macro-indentation technique, profilometer-based indentation plastometry (PIP), is utilized to capture the variation in the mechanical properties in the feedstock (rolled plate) and weld region. The feedstock exhibited a gradient microstructure owing to different stress states during rolling. Additionally, the stirring of the tool resulted in a complex material flow pattern producing a microstructure gradient in the weld region. The aspect ratio of the grains was identified as the primary factor causing variation in indentation response in the feedstock. In contrast, variation in the grain size in the weld region resulted in property variation. Overall, the stir zone has a lower yield strength (158±4MPa) than the base material (173±3MPa). In addition, microstructure heterogeneity in the stir zone resulted in lower elongation than the base material.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Final Report UCLA-Thermochemical Storage with Anhydrous Ammonia

In ammonia-based thermochemical energy storage (TCES), ammonia is dissociated endothermically as it absorbs solar energy during the daytime. When energy is required, the reverse reaction releases energy to heat a working fluid such as steam, to produce electricity. Ammonia-based TCES has great advantages of simplicity, low cost reactants, and a strong industrial base in the conventional ammonia industry. The concept has been demonstrated over three decades of research at Australian National University, achieving a 24-hour demonstration of a complete system. At the start of this project, three challenges were identified that would have to be addressed to show that the system is technically and economically viable for incorporation into a CSP plant with an advanced, high temperature power block. All three of these challenges have now been addressed: 1. The ammonia synthesis reaction had not, to our knowledge, been carried out at temperatures consistent with modern power blocks (i.e., ~650°C). The technical feasibility of operating a reactor under high-temperature, near-equilibrium conditions was an unknown, and was therefore a technical risk. The project has successfully demonstrated steam heating to 650°C and energy recovery to steam at the 5 kWt level. 2. The ammonia system has a relatively low enthalpy of reaction combined with gas phase reactants. This is not a direct disadvantage since the reactants themselves are low cost. The challenge lies in storing the required volume of reactants cost effectively. Therefore, a second key goal was to show, through techno-economic analysis, that underground storage technologies can be used to store the energy-rich gas at a cost that is consistent with the SunShot cost goal. We have identified two promising technologies for gas storage: storage in salt caverns has an estimated cost of 1(USD)/kWht and storage in drilled shafts could be on the order of 7(USD)/kWht. Together these two options answer the technical challenge associated with storage of gas phase components. 3. While this project is primarily concerned with high-temperature heat recovery and methods to store the gaseous components, it is also important to consider the feasibility of the entire system. Consequently, an additional goal was to perform analysis to show the feasibility of integrating endothermic reactors within a tower receiver. A conceptual design of an ammonia dissociation receiver/reactor has been developed that fits into the same size cylindrical envelope as the molten salt receiver in SAM, and has the same design thermal capacity. The calculated thermal efficiency of this receiver is 94.6%. Thus, this investigation has established the technical feasibility of a surround field tower system using ammonia dissociation. With these challenges addressed, we proceeded to design a full-scale synthesis and heat recovery system. A model was developed and validated by comparison with our experimental data. A parametric study showed, among other things, the importance of using small tube diameters and spacing to enhance heat transfer. Multi-parameter optimization was used to find a design that minimizes the wall material volume. Finally, cost estimation shows that the ammonia system has good prospects of meeting the Sunshot 15(USD)/kWht target: estimated costs of the entire synthesis system for the 220 MWt plant with 6 hours of storage are 13(USD)/kWht using salt cavern storage and 18(USD)/kWht using shaft drilling. Costs per kWht are even lower with more hours of storage. With the established technology of ammonia synthesis as a starting point, the successes of the project have mitigated technical risks associated with high-temperature synthesis reaction, underground storage, and tower receiver design. Estimated costs are less than 15(USD)/kWht with salt cavern storage. It is now possible to map a time line to commercial deployment that is likely to be shorter and less risky than other thermochemical cycles under active investigation. UCLA has filed a patent that protects the new ideas developed during this project. Discussions are ongoing with potential investors with the aim of partnering for further work. As well as immediate improvements and extra work with the existing experimental system, a key goal is to extend it to a small solar-driven project at an early opportunity.

14 SOLAR ENERGY↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
DOE OSTI2022

Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method

We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread. Previous approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver, which restricts the scalability of this approach. In this work, we overcome this restriction by designing an algorithm that adapts the multiplicative weights update (MWU) framework, along with the sample average approximation (SAA) technique, to approximately solve the linear program (LP) relaxation for the problem. To scale this approach further, we provide a memory-efficient algorithm that enables scaling to large networks, corresponding to country-size populations, with over 300 million nodes and 30 billion edges. Furthermore, we show that this approach provides near-optimal solutions to the LP in practice.

Sambaturu, Prathyush↗
DOE OSTI2018

Chapter 4 - Mechanical Fractionations of Biomass Feedstocks for Enhanced Pretreatment and Conversion. In the book Biomass Preprocessing and Pretreatments for Production of Biofuels: Mechanical, Chemical and Thermal Methods.

Living plant biomass consists of multiple types of tissues and organs, each performing unique and essential physical or biological functions in the living plant. Because of these differences in form and function, these tissues and organs can retain these unique physical and chemical properties after senescence or harvest. Properties such as ash composition, ash content, carbohydrate content, lignin content, density, and recalcitrance can vary widely between plant parts and tissues. Mechanical fractionation of biomass feedstocks has the potential to take advantage of these naturally occurring differences to produce physically or chemically unique fractions that have higher or lower value within specific biochemical or thermochemical conversion platforms. Mechanical fractionation techniques that have shown promise include density separations in air or liquid, size separations, and automated sorting. Density separations, such as air classification, separate based on particle density, size, and shape. Size separations are highly dependent upon size reduction techniques, however anatomical differences can affect particle size. The chemical and physical properties of the resulting fractions help determine their optimal use. These unique fractions can be used directly or treated as ingredients and blended with other feedstocks or fractions of feedstocks to meet cost, quality, or conversion specifications.

09 BIOMASS FUELS↗
DOE OSTI2023

Analysis of neptunium oxides produced through modified direct denitration

Production of neptunium-237 ( 237 Np) target materials for plutonium-238 ( 238 Pu) radioisotope thermoelectric generators (RTGs) for deep space exploration requires advanced chemistry and engineering development. Currently, the domestic Pu-238 Supply Program at Oak Ridge National Laboratory produces neptunium dioxide (NpO 2 ) for target material using a modified direct denitration (MDD) flowsheet. Although the chemistry, reaction mechanisms, and product characteristics of MDD are well understood for uranium, corresponding studies of the neptunium system are still needed to continue optimization of target material properties, production equipment design, and production flowsheets. Here, the objective of this work is to characterize crystalline phases, morphology, surface texture, and particle size of NpO 2 produced via MDD reactions. Solid-phase characterization techniques, including powder X-ray diffraction (pXRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), were employed to achieve this objective. Subsequent data processing using the Morphological Analysis for Material Attribution (MAMA) software was performed to analyze particle morphology and size. Broadly, the powders were found to contain a mixture of NpO 2 and Np 2 O 5 after denitration with a variety of morphologies. After high-firing, the product was found to be NpO 2 with a typical polycrystalline oxide morphology and a grain size ranging from 0.72 to 0.94 µm. These analyses provide knowledge on the reaction pathway for a non-traditional NpO 2 synthesis method and offer additional unique insight into production-scale environments for transuranic materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2010

An High Resolution Near-Earth Objects Population Enabling Next-Generation Search Strategies

Over the past decade, the dedicated search for kilometer-size near-Earth objects (NEOs), potentially hazardous objects (PHOs), and potential Earth impactors has led to a boost in the rate of discoveries of these objects. The catalog of known NEOs is the fundamental ingredient used to develop a model for the NEOs population, either by assessing and correcting for the observational bias (Jedicke et al., 2002), or by evaluating the migration rates from the NEOs source regions (Bottke et al., 2002). The modeled NEOs population is a necessary tool used to track the progress in the search of large NEOs (Jedicke et al., 2003) and to try to predict the distribution of the ones still undiscovered, as well as to study the sky distribution of potential Earth impactors (Chesley & Spahr, 2004). We present a method to model the NEOs population in all six orbital elements, on a finely grained grid, allowing us the design and test of targeted and optimized search strategies. This method relies on the observational data routinely reported to the Minor Planet Center (MPC) by the Catalina Sky Survey (CSS) and by other active NEO surveys over the past decade, to determine on a nightly basis the efficiency in detecting moving objects as a function of observable quantities including apparent magnitude, rate of motion, airmass, and galactic latitude. The cumulative detection probability is then be computed for objects within a small range in orbital elements and absolute magnitude, and the comparison with the number of know NEOs within the same range allows us to model the population. When propagated to the present epoch and projected on the sky plane, this provides the distribution of the missing large NEOs, PHOs, and potential impactors.

Tricaico, Pasquale↗
DOE OSTI2019

Self-powered Wireless Dual-mode Langasite Sensor for Pressure/Temperature Monitoring of Nuclear Reactors

Safety issues in nuclear power plants are of extreme importance. Numerous sensors are used within nuclear power plants for sensing and monitoring of its critical operation parameters. As currently implemented, during massive losses of power across the plant in accidents, the backup batteries for the monitoring sensors will ultimately be drained and can cease to operate. The overall objective of this NEUP project is to develop a novel self-powered wireless hybrid sensor which can accurately monitor both pressure and temperature using a single device, even in the extreme harsh environments of severe nuclear accidents, without the requirement of external electricity. At Virginia Tech side, a heat pipe assisted thermoelectric generator (TEG) energy harvester and associated power management and data transmission circuits were developed. The energy harvester can produce electrical power continuously, even in loss of plant and external power situations, providing power for important data transmission for accident diagnosis. The self-powered sensing system could be integrated directly onto key nuclear components including pipes, pump housings, heat exchangers, reactor vessels, and shielding structures inside and outside of the reactor core. Two TEG based energy harvesters which can work in relatively low (50-250 ºC) and high temperature ranges (250-350 ºC) were established and tested. In the prototypes, heat pipes were introduced to reduce the heat resistances of the device at the hot and cold ends. The power output of the heat pipe assisted TEG module is 2.22 W, which is 6 times more than one using an aluminum rod. In-lab tests showed that these devices canmeet the requirement to power the sensors and circuit during normal and off-normal conditions.Amodel was then built to analyze the thermal network in the energy harvester, and was used to optimize its performance and reduce its geometry size. The design and testing of the vibrational energy harvester were completed. The harvester, has shown to effectively increase the vibrational potential of the main coolant pump, though it has been shown that thermal energy has a much higher potential for the powering of sensors. In this project, a self-start charger circuit for the energy harvester was designed to regulate and manage the power. An energy storage element was incorporated into the energy harvesting module for sensors and wireless transceivers in an off-normal situation. The signal conditioning and wireless communication electronic circuits were developed and tested in the lab. An important consideration for an energy harvester within a nuclear power plant is the level of radiation it will encounter during operation. The two main sources of radiation in a nuclear power plant are gamma and neutron radiation. Aradiation experiment was conducted in the Westinghouse Electric Company during which the prototypes were placed in a 106 rad radiation environment to test their ability to operate in the extreme environment. The results showed that this level of radiation had little effect on the performance of the TEGmaterial and heat pipe heat exchanging system. Its influence on the circuit was still under research, however the need for more development on radiation hardened electronics is needed. Pressure measurement based on piezoelectric crystal resonators has advantages over the commercially available methods like capacitive, electromagnetic, optical, potentiometric, and piezo resistive strain gauges. Since the output of piezoelectric based sensors are digital in format, it has a very high resolution, high accuracy, and long-term stability. Quartz is one of the commonly used piezoelectric material in digital pressure sensors design. This material is elastic in nature, providing high stability, high repeatability, high elasticity, repeatable mechanical behavior for many cycles of operation, and is free of hysteresis. The quartz resonator has a high Q, which means that very high vibration can be driven with the very low electric power. Langasite (LGS) is a new promising piezoelectric material that combines the advantageous properties of quartz with a better performance at high temperatures. LGS will not undergo any phase transitions up to its melting temperature of 1473 C, and hence, the application of Langasite in measuring pressure at high temperatures is promising. In this project, the design and fabrication of the piezo oscillator crystal for temperature and pressure measurement has been completed. The entire sensor has been fabricated and tested to see its sensitivity to temperature and pressure changes. The design and optimization of the radiation shielding systems that can effective protect the duel-mode Langasite sensor inside reactor core were demonstrated. To maximize the radiation shielding effectiveness, different types of materials are investigated based upon recent developments of radiation shielding systems including aluminum + Boron carbide and bismuth borosilicate glass B2O3 in addition to tungsten/B4C. New materials based upon metatheses is included as potential shielding to mitigate neutron fluencies. The radiation shielding effectiveness of selected composites are evaluated within a broader neutron spectrum from thermal, epithermal, fast (1 MeV) and 15 MeV neutrons. Performance analysis was also conducted to evaluate the effects of selected composite systems.Aconceptual design was also proposed for the packaging and integration of the radiation shielding system with the sensor, and its applicability within the reactor core was discussed. At end of the project, all components necessary for a complete sensing and monitoring system, including a langasite sensor, electrodes, the TEG harvester, an energy storage device, and power management and transmission electronics were integrated to demonstrate feasibility in powering the sensor. Finally, integration of each component into a full package, and a full demonstration were done.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Laser Powder Deposition Parametric Optimization and Property Development for Ti-6Al-4V Alloy

In this study, Ti6Al4V alloy was produced via laser powder deposition (LPD). To obtain Ti6Al4V alloy with maximum density, LPD parameters for preparing Ti6Al4V samples were optimized using the Taguchi method. Results were analyzed on the basis of the signal-to-noise (S/N) ratios and analyses of variance. A high energy density should be used to achieve higher levels of densification. The optimal combination of parameters for density was a scanning speed of 550 mm/min, laser power of 160 W, powder feeding rate of 0.99 g/min, and shield gas flow of 8 L/min. An almost fully dense Ti6Al4V sample was prepared using the optimized LPD process, and the relative density was greater than 99%. In addition, the microstructure and properties of Ti6Al4V samples prepared by optimized LPD process were investigated. The microstructure investigation revealed that the LPD-prepared Ti6Al4V sample was predominantly composed of fine acicular α phase and lath-type α phase. Tensile and microhardness tests indicated that the LPD sample had higher mechanical properties than the traditional cast Ti6Al4V alloy because of the acicular martensitic phase and smaller grain size.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ChemPren: a new and economical technology for conversion of waste plastics to light olefins

With the ever-increasing demand for plastics, sustainable recycling methods are key necessities. Here, the current plastics industry can manage to recycle only 10% of the 400 million metric tons of plastic produced globally. Waste plastics, in the current infrastructure, land up mostly in landfills. Although a lot of research efforts have been spent on processing and recycling co-mingled mixed plastics, energy-efficient sustainable and scalable routes for plastic upcycling are still lacking. Catalytic valorization of waste plastic feedstock is one of the potential scalable routes for plastic upcycling. Silica-alumina based materials, and zeolites have shown a lot of promise. A major interest lies in restricting catalyst deactivation, and refining product selectivity and yield for such catalytic processes. This article highlights ChemPren technology as a clean energy solution to waste plastic recycling. Co-mingled, mixed plastic feedstock along with spray dried, attrition resistant, ZSM-5 containing catalysts is preprocessed with an extruder to form optimally sized particles and fed into a fluidized bed reactor for short contact times to produce selectively and in high yields ethylenes, propylenes and butylenes. This techno-economic perspective indicates that the ChemPren technology can produce propylene at $\$$0.16 per lb, whereas the current selling price of virgin propylene is $0.54 per lb. This technology can serve as a platform for mixed plastic upcycling, with more advancements necessary in the form of robust and resilient catalysts and reactor operation strategies for tuning product selectivity.

25 - ENERGY STORAGE↗
DOE OSTI2021

Increased seminal root number associated with domestication improves nitrogen and phosphorus acquisition in maize seedlings

Abstract Background and Aims Domesticated maize (Zea mays ssp. mays) generally forms between two and six seminal roots, while its wild ancestor, Mexican annual teosinte (Zea mays ssp. parviglumis), typically lacks seminal roots. Maize also produces larger seeds than teosinte, and it generally has higher growth rates as a seedling. Maize was originally domesticated in the tropical soils of southern Mexico, but it was later brought to the Mexican highlands before spreading to other parts of the continent, where it experienced different soil resource constraints. The aims of this study were to understand the impacts of increased seminal root number on seedling nitrogen and phosphorus acquisition and to model how differences in maize and teosinte phenotypes might have contributed to increased seminal root number in domesticated maize. Methods Seedling root system architectural models of a teosinte accession and a maize landrace were constructed by parameterizing the functional–structural plant model OpenSimRoot using plants grown in mesocosms. Seedling growth was simulated in a low-phosphorus environment, multiple low-nitrogen environments, and at variable planting densities. Models were also constructed to combine individual components of the maize and teosinte phenotypes. Key Results Seminal roots contributed ~35 % of the nitrogen and phosphorus acquired by maize landrace seedlings in the first 25 d after planting. Increased seminal root number improved plant nitrogen acquisition under low-nitrogen environments with varying precipitation patterns, fertilization rates, soil textures and planting densities. Models suggested that the optimal number of seminal roots for nutrient acquisition in teosinte is constrained by its limited seed carbohydrate reserves. Conclusions Seminal roots can improve the acquisition of both nitrogen and phosphorus in maize seedlings, and the increase in seed size associated with maize domestication may have facilitated increased seminal root number.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Sea surface velocities from visible and infrared multispectral atmospheric mapping sensor imagery

High resolution (100 m), sequential Multispectral Atmospheric Mapping Sensor (MAMS) images were used in a study to calculate advective surface velocities using the Maximum Cross Correlation (MCC) technique. Radiance and brightness temperature gradient magnitude images were formed from visible (0.48 microns) and infrared (11.12 microns) image pairs, respectively, of Chandeleur Sound, which is a shallow body of water northeast of the Mississippi delta, at 145546 GMT and 170701 GMT on 30 Mar. 1989. The gradient magnitude images enhanced the surface water feature boundaries, and a lower cutoff on the gradient magnitudes calculated allowed the undesirable sunglare and backscatter gradients in the visible images, and the water vapor absorption gradients in the infrared images, to be reduced in strength. Requiring high (greater than 0.4) maximum cross correlation coefficients and spatial coherence of the vector field aided in the selection of an optimal template size of 10 x 10 pixels (first image) and search limit of 20 pixels (second image) to use in the MCC technique. Use of these optimum input parameters to the MCC algorithm, and high correlation and spatial coherence filtering of the resulting velocity field from the MCC calculation yielded a clustered velocity distribution over the visible and infrared gradient images. The velocity field calculated from the visible gradient image pair agreed well with a subjective analysis of the motion, but the velocity field from the infrared gradient image pair did not. This was attributed to the changing shapes of the gradient features, their nonuniqueness, and large displacements relative to the mean distance between them. These problems implied a lower repeat time for the imagery was needed in order to improve the velocity field derived from gradient imagery. Suggestions are given for optimizing the repeat time of sequential imagery when using the MCC method for motion studies. Applying the MCC method to the infrared brightness temperature imagery yielded a velocity field which did agree with the subjective analysis of the motion and that derived from the visible gradient imagery. Differences between the visible and infrared derived velocities were 14.9 cm/s in speed and 56.7 degrees in direction. Both of these velocity fields also agreed well with the motion expected from considerations of the ocean bottom topography and wind and tidal forcing in the study area during the 2.175 hour time interval.

Pope, P. A.↗
NASA NTRS1996

The Newton Modified Barrier Method for QP Problems

The Modified Barrier Functions (MBF) have elements of both Classical Lagrangians (CL) and Classical Barrier Functions (CBF). The MBF methods find an unconstrained minimizer of some smooth barrier function in primal space and then update the Lagrange multipliers, while the barrier parameter either remains fixed or can be updated at each step. The numerical realization of the MBF method leads to the Newton MBF method, where the primal minimizer is found by using Newton's method. This minimizer is then used to update the Lagrange multipliers. In this paper, we examine the Newton MBF method for the Quadratic Programming (QP) problem. It will be shown that under standard second-order optimality conditions, there is a ball around the primal solution and a cut cone in the dual space such that for a set of Lagrange multipliers in this cut cone, the method converges quadratically to the primal minimizer from any point in the aforementioned ball, and continues to do so after each Lagrange multiplier update. The Lagrange multipliers remain within the cut cone and converge linearly to their optimal values. Any point in this ball will be called a "hot start". Starting at such a "hot start", at most Omicron(1n 1n epsilon(exp -1)) Newton steps are sufficient to perform the primal minimization which is necessary for the Lagrange multiplier update. Here, epsilon > 0 is the desired accuracy. Because of the linear convergence of the Lagrange multipliers, this means that only Omicron(1n epsilon(exp -1))omicron(ln 1n epsilon(exp-1)) Newton steps are required to reach an epsilon-approximation to the solution from any "hot start". In order to reach the "hot start", one has to perform Omicron(square root(m) 1n C) Newton steps, where m characterizes the size of the problem and C > 0 is the condition number of the QP problem. This condition number will be characterized explicitly in terms of key parameters of the QP problem, which in turn depend on the input data and the size of the problem.

Melman, A.↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2020

Metric Type in the Target-matrix Mesh Optimization Paradigm

The Target Matrix Optimization Paradigm (TMOP) is a method for improving the accuracy, efficiency, and robustness of numerical solutions to partial differential equations by improving the geometric quality of the computational mesh, primarily through node movement. TMOP has been successfully applied to a number applications even though the paradigm was not fully understood at the time. With this work, TMOP can be seen to be a tightly woven fabric of interconnecting ideas and concepts that provides a powerful approach to mesh optimization. The central unifying concepts in TMOP are the concept of a Target Matrix and the concept of Metric Type. Target matrices are motivated by the desire to make mesh quality improvement application-specific and, when needed, solution-adaptive. Metric type plays an essential role because it provides, through the use of typed metrics, the bridge between application-specific quality and target construction. It is shown that there are eight theoretical metric types, including the shape and shape+size types used informally in the past. It is shown further that there exist well-posed metrics corresponding to six of the eight metric types. A well-posed metric is a metric that is typed and convex, polyconvex, or invex, and further, it is a metric that simplifies target construction.

97 MATHEMATICS AND COMPUTING↗