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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Distribution System State Estimation Using a Multiple Iteration Extended Kalman Filter Approach

To support the operation of modern distribution systems, operators require real-time visibility into system states. Due to a lack of measurements and unbalanced operation, the state estimation in distribution systems is challenging as compared to transmission systems. This paper proposes the utilization of a Multiple Iteration - Extended Kalman Filter based approach for the distribution system state estimation. This modified version of the baseline extended Kalman filter iterates over the update step multiple times thereby reducing the estimation error. The proposed algorithm along with the auxiliary algorithms such as bad data detection is integrated into a co-simulation environment. Case studies show that the proposed state estimation method can result in a lesser estimation error as compared to the baseline approach.

Bhatti, Bilal Ahmad↗

DOE OSTI2021

Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. Finally, it is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLPD-OPF model for the same distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1982

Modification and evaluation of a Barnes-type objective analysis scheme for surface meteorological data

The Purdue Regional Objective Analysis of the Mesoscale (PROAM) is a Barness-type scheme for the analysis of surface meteorological data. Modifications are introduced to the original version in order to increase its flexibility and to permit greater ease of usage. The code was rewritten for an interactive computer environment. Furthermore, a multiple iteration technique suggested by Barnes was implemented for greater accuracy. PROAM was subjected to a series of experiments in order to evaluate its performance under a variety of analysis conditions. The tests include use of a known analytic temperature distribution in order to quantify error bounds for the scheme. Similar experiments were conducted using actual atmospheric data. Results indicate that the multiple iteration technique increases the accuracy of the analysis. Furthermore, the tests verify appropriate values for the analysis parameters in resolving meso-beta scale phenomena.

Smith, D. R.↗

DOE OSTI2022

Implementation of an Orificing Optimization Algorithm in the DASSH Subchannel Analysis Code

The Ducted Assembly Steady-State Heat transfer code (DASSH) performs full-core subchannel thermal hydraulics calculations in liquid metal fast reactors. One of the applications of subchannel codes is to optimize coolant flow orificing. As a design activity, the primary task is to determine the best way to divide assemblies into groups and distribute coolant flow rates among them. This report documents an algorithm implemented in DASSH to automatically optimize coolant orificing. Over the course of multiple iterations, DASSH determines the orifice grouping and flow distribution that minimizes peak coolant, clad, or fuel temperatures across all timesteps for a user-specified number of assembly groups. The total coolant flow rate in the reactor is constrained to achieve the specified core-average outlet temperature. The flow rate to each orifice group may also be constrained by the allowable pressure drop. The distribution of coolant flow among groups is accelerated using a predictor-corrector algorithm based on interpolated results from single-assembly parametric calculations. The assembly orificing grouping is initially predicted based on assembly power but can be refined if results demonstrate that an assembly would fit better in another group. The algorithm is demonstrated with two case studies. The first is a simple model for a reactor core consisting of just fuel assemblies; the pin power distributions are specified to create a situation where the initial assembly grouping prediction is suboptimal. This example is used to describe the initial grouping, demonstrate convergence over multiple iterations, and highlight the impact of regrouping. Then, the algorithm is applied to minimize peak clad and fuel temperatures in an example sodium-cooled fast reactor, the Versatile Test Reactor. The multicycle optimization confirms prior calculations for the reference core design. The example highlights how optimizing for different peak temperatures affects the results and demonstrates the use of the pressure drop constraint to limit the maximum flow rate.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Characterization and Optimization of the Fitting of Quantum Correlation Functions

This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.

Chuang, Pi-Yueh [Virginia Tech,Dept. of Computer S↗

NASA NTRS2009

Perl Modules for Constructing Iterators

The Iterator Perl Module provides a general-purpose framework for constructing iterator objects within Perl, and a standard API for interacting with those objects. Iterators are an object-oriented design pattern where a description of a series of values is used in a constructor. Subsequent queries can request values in that series. These Perl modules build on the standard Iterator framework and provide iterators for some other types of values. Iterator::DateTime constructs iterators from DateTime objects or Date::Parse descriptions and ICal/RFC 2445 style re-currence descriptions. It supports a variety of input parameters, including a start to the sequence, an end to the sequence, an Ical/RFC 2445 recurrence describing the frequency of the values in the series, and a format description that can refine the presentation manner of the DateTime. Iterator::String constructs iterators from string representations. This module is useful in contexts where the API consists of supplying a string and getting back an iterator where the specific iteration desired is opaque to the caller. It is of particular value to the Iterator::Hash module which provides nested iterations. Iterator::Hash constructs iterators from Perl hashes that can include multiple iterators. The constructed iterators will return all the permutations of the iterations of the hash by nested iteration of embedded iterators. A hash simply includes a set of keys mapped to values. It is a very common data structure used throughout Perl programming. The Iterator:: Hash module allows a hash to include strings defining iterators (parsed and dispatched with Iterator::String) that are used to construct an overall series of hash values.

Tilmes, Curt↗

NASA NTRS1980

On the security of the Merkle-Hellman cryptographic scheme

Cryptanalysis applied to a simple Merkle-Hellman (1978) public key cryptographic system (described within a standard-parameter knapsack problem) reveals the high vulnerability of the system. However, a cryptanalytic attack becomes ineffective when the knapsack problem with two or more iterations is used in order to obscure the structure of the superincreasing sequence. To enhance the security of a cryptographic system structured numbers can be used, while the multiple iterations of the modular multiplications technique can produce a safer Merkle-Hellman knapsack system.

Shamir, A.↗

NASA NTRS2016

Instrumentation for Examining Microbial Response to Changes In Environmental Pressures

The Automated Adaptive Directed Evolution Chamber (AADEC) is a device that allows operators to generate a micro-scale analog of real world systems that can be used to model the local-scale effects of climate change on microbial ecosystems. The AADEC uses an artificial environment to expose cultures of micro-organisms to environmental pressures, such as UV-C radiation, chemical toxins, and temperature. The AADEC autonomously exposes micro-organisms to selection pressures. This improves upon standard manual laboratory techniques: the process can take place over a longer period of time, involve more stressors, implement real-time adjustments based on the state of the population, and minimize the risk of contamination. We currently use UV-C radiation as the main selection pressure, UV-C is well studied both for its cell and DNA damaging effects as a type of selection pressure and for its related effectiveness as a mutagen; having these functions united makes it a good choice for a proof of concept. The AADEC roadmap includes expansion to different selection pressures, including heavy metal toxicity, temperature, and other forms of radiation.The AADEC uses closed-loop control to feedback the current state of the culture to the AADEC controller that modifies selection pressure intensity during experimentation, in this case culture density and growth rate. Culture density and growth rate are determined by measuring the optical density of the culture using 600 nm light. An array of 600 nm LEDs illuminate the culture and photodiodes are used to measure the shadow on the opposite side of the chamber.Previous experiments showed that we can produce a million fold increase to UV-C radiation over seven iterations. The most recent implements a microfluidic system that can expose cultures to multiple different selection pressures, perform non-survival based selection, and autonomously perform hundreds of exposure cycles. A scalable pump system gives the ability to pump in various different growth media to individual cultures and introduce chemical toxins during experimentation; AADEC can perform freeze and thaw cycles. We improved our baseline characterization by building a custom UV-C exposure hood, a shutter operates on a preset timer allowing the user to set exposure intensity consistently for multiple iterations.

Environment↗

NASA NTRS1988

A hardware implementation of a relaxation algorithm to segment images

Relaxation labelling is a mathematical technique frequently applied in image processing algorithms. In particular, it is extensively used for the purpose of segmenting images. The paper presents a hardware implementation of a segmentation algorithm, for images consisting of two regions, based on relaxation labelling. The algorithm determines, for each pixel, the probability that it should be labelled as belonging to a particular region, for all regions in the image. The label probabilities (labellings) of every pixel are iteratively updated, based on those of the pixel's neighbors, until they converge. The pixel is then assigned to the region correspondent to the maximum label probability. The system consists of a control unit and of a pipeline of segmentation stages. Each segmentation stage emulates in the hardware an iteration of the relaxation algorithm. The design of the segmentation stage is based on commercially available digital signal processing integrated circuits. Multiple iterations are accomplished by stringing stages together or by looping the output of a stage, or string of stages, to its input. The system interfaces with a generic host computer. Given the modularity of the architecture, performance can be enhanced by merely adding segmentation stages.

Loda, Antonio G.↗

DOE OSTI2021

Integrated model predictions on the impact of substrate damage on gas dynamics during ITER burning-plasma operations

Divertor design and choice of plasma-facing materials (PFM) will be essential to the success of next-generation fusion reactors as they operate under more powerful scenarios. Understanding and controlling interactions between the plasma and PFM is essential to making these choices. Within these plasma–material interactions and especially in tungsten (W), the interplay between the most abundant plasma species (hydrogen isotopes and helium, He) with the wall material alters fuel retention. However, this interplay is yet to be sufficiently understood to confidently project fuel retention levels to future fusion devices. The paper presents a series of integrated simulations of fusion plasmas and their interaction with tungsten. Specifically, this study assesses the impact of He plasma pre-exposure on hydrogenic species retention during 100 s of burning plasma operations (BPO) in ITER. Multiple pre-exposure scenarios are considered, including sub-surface damage resulting from exposures in the linear device PISCES and from early ITER He-operation. The predictions from these consecutive He-BPO exposures show that fuel content and spatial distribution in the material are largely determined by the He-induced damage, as manifest in: (i) changes in surface temperature expected during BPO have little effect on fuel retention in the presence of He-induced damage; (ii) gas content stabilizes quickly in substrates pre-exposed in PISCES, at levels set by the concentration of pre-existing vacancies, while it continues to increase in substrates initially pristine or pre-exposed to ITER He plasmas; (iii) the presence of He and He–V clusters in the near-surface region locally increases hydrogenic retention, but decreases its permeation; this results in hydrogenic species that remain closer to the surface in pre-damaged substrates, while the bulk content is higher for initially pristine cases. In summary, the interaction and binding of D and T with the pre-existing He–V clusters modifies retention and permeation of hydrogen species during ITER BPO.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Optimal Iteration and its Application to Some Problems in Aerosol Science and Particle Dynamics

Iteration is a common technique for finding the solutions to an equation. It is easy to code, straightforward to apply, readily comprehensible, and can be run indefinitely until a given accuracy is attained. However, for a given equation there are multiple iteration schemes that can be employed, with different convergence rates, and there is no obvious way to determine a priori which is best. In this work, the convergence rates of different approaches to simple iteration schemes are analyzed and the new technique of optimal iteration, which determines the scheme that maximizes the convergence rate, is introduced and illustrated by its application to several common problems in aerosol and particle dynamics. The first application is determination of the mobility diameter of an aerosol particle from the measured mobility, which is complicated by the nonlinearity of the Cunningham correction. This same equation occurs in the determination of the diameter of multiply-charged particles with the same mobility diameter as singly-charged particles. The next application is determination of the aerodynamic diameter from the mobility diameter for situations in which the Cunningham correction must be taken into account. The final two applications are determination of the terminal velocity from the diameter, and of the diameter from the terminal velocity, for particles sufficiently large that Stokes’ Law does not apply. The technique is easy to apply and can be employed in a number of situations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Design and Analysis of Shape Memory Spring Tires for Martian and Lunar Rover Vehicles

Shape memory alloys (SMAs) have played an important role in various innovative engineering and medical applications, such as aerospace actuators, vibration damping devices, and coronary stents. In applications, shape memory alloys are commonly utilized in two fundamentally different ways: (i) making use of the superelasticity/ pseudoelasticity (SE/PE) phenomena, as in applications in biomedical engineering, and (ii) taking advantages of the shape memory effect (SME), as is used for actuators. Their ability to act in such vastly different capacities is mainly due to their unique capability to recover large amounts of deformation produced by either applied stresses or temperature changes. One recent emerging application in use of SMAs has been in the area of non-pneumatic tire designs for Martian or Lunar roving vehicles. These vehicles require tires that are capable of traversing rugged terrain while withstanding extreme temperatures and atmospheric conditions. Inspired by the flexible wire mesh tires used on three Lunar Roving Vehicle (LRV) missions to the Moon on Apollo 15, 16, and 17, a new compliant tire technology was developed by the NASA Glenn Research Center (GRC) and Goodyear Tire & Rubber, known as the Spring Tire. The Spring Tire consists of several hundred coiled springs woven into a flexible mesh and formed into the shape of a tire. Like the LRV wire mesh tires, the original Spring Tires were made from spring steel and were prone to permanent deformation when undergoing high localized loads. Later, a new iteration of this technology was invented, known as the ‘Superelastic Tire’. This new technology incorporated the use of superelastic SMA springs, which could effectively undergo approximately 30 times more reversible deformation than the steel spring. It also provided even greater durability and allowed for more flexibility in design, such as the use of other structural forms to reduce mass or increase load carrying capacity. Because of the unique nature of both the SMA material and the complex interactions between the springs, designing Spring Tires for a specific application requires extensive effort. Historically, design decisions have relied on full-scale empirical testing; however, this is very expensive and time consuming, especially when multiple iterations are needed. Therefore, developing a large-scale, robust, and predictive numerical model entailing complex spring interactions and the shape memory material behavior within a tire construct is the first essential step towards a successful design program. The current work focuses on implementation of the user-defined Shape Memory Alloy (SMA) model, otherwise known as SMA-GVIPs, in the Finite Element analysis (FEA) program ABAQUS for large-scale simulations of the GRC-developed Spring Tires made of SMA. The novelty of this work lies in the thorough, detail-oriented, and computationally efficient finite element analysis of full-scale SMA tires. A well-thought material characterization plan followed by model validation and a systemic sensitivity study on SMA tires has never been reported in the previous literature. The main objective of this study is to help the team improve and optimize the structural design of the SMA tires through in-depth numerical analysis and sensitivity studies. Various design variables (wire diameter, coil diameter, pitch, bead angle, and number of springs) were varied to study their influence on the global load-displacement response of the tire construct. A detailed investigation of the three-dimensional stress states was also carried out to enhance our understanding of the local changes as the tire goes through global deformation. It was concluded that a robust numerical model with a good predictive capability, together with a thoughtfully crafted sensitivity study can result in improved design iterations required to reach a desired tire performance while, significantly reducing manufacturing, labor and testing expenses. A summary of the Finite Element (FE) model construction will be presented together with a description of the user-defined SMA model, characterization process, experimental results, model validation, and numerical sensitivity study results.

shape memory alloys↗

DOE OSTI2024

The CXSFIT spectral fitting code: Past, present and future

Magnetically confined plasma experiments generate a wealth of spectroscopic data. The first step toward extracting physical parameters is to fit a spectral model to the often complex spectra. The CXSFIT (Charge eXchange Spectroscopy FITting) spectral fitting code was originally developed for fitting charge exchange spectra on JET from the late 1980s onward and has been further developed over decades to keep up with the needs of the users. The primary use is to efficiently fit a large number of spectra with many constrained Gaussian spectral lines of which the physical parameters can be coupled in a user-friendly manner. More recent additions to the code include time-dependent couplings between parameters, flexible background subtraction, and a non-linear coupling scheme between fit parameters. The latter was a pre-requisite for implementing Zeeman and motional Stark effect multiplets in the library of spectral features. The ability to save and replay “fit recipes,” even when multiple iterations are required, has ensured the traceability of the results and is one of the keys to the longevity and success of the code. The code is also in use on other tokamaks (AUG, ST-40) and to fit data from other spectroscopic diagnostics on JET. In this paper, we document the current capabilities and philosophy behind the structure of the code, including some of the algorithms used to calculate spectral features numerically efficiently. We also provide an outline of how CXSFIT could be transferred into a framework that would be able to meet the spectral fitting requirements of future devices, such as ITER.

Delabie, Ephrem G.↗

DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗

DOE OSTI2023

Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

Jeske, Torri↗

DOE OSTI2022

BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗

DOE OSTI2025

Sequential Stress Identifies Processing Defects in Bifacial Photovoltaic Modules That Limit Durability

Here, we use sequential stress to investigate hurdles to bifacial photovoltaic (PV) module durability from lamination defects. We test mini-modules with glass/glass (G/G) and glass/transparent-backsheet (G/TB) constructions using either ethylene vinyl acetate or polyolefin elastomer (POE) based encapsulants under a modified IEC 63209-2 sequential stress. This sequence includes multiple iterations of damp heat (DH200), full spectrum light exposure (A3), thermal cycling (TC50), and humidity/freeze (HF10). We compare indoor stress with outdoor exposure. Results show similar relative trends in degradation after a year outdoors compared to our first stress cycle. Subsequent stress cycles impart more severe damage than outdoor exposure for the short outdoor duration used here. Edge-pinch lamination defects in G/G mini-modules limit durability causing delamination and cell cracks. Conversely, we observe greater degradation in G/TB mini-modules compared to G/G in the later stages of the stress sequence when the backsheets are directly exposed to UV-containing light. Our results highlight: 1) the utility of sequential stress testing to uncover degradation modes in bifacial PV, 2) implications of using mini-modules for testing PV quality, and 3) the importance of lamination defects that must be avoided to ensure durability as the industry adopts G/G or G/TB packaging.

14 SOLAR ENERGY↗