Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “randomization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2021

Leveraging Randomized Compiling for the QITE Algorithm

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1\%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

Ville, Jean-Loup↗
DOE OSTI2023

Late Breaking Results: COPPER: Computation Obfuscation by Producing Permutations for Encoding Randomly

Deployed embedded devices face security risks due to increased ease of physical access to the devices by unauthorized users. Capable adversaries can intercept a device to recover the data in memory, including results of performed sensitive computations. Device owners require data confidentiality on their physically insecure devices. To satisfy this goal we implement a novel method, COPPER (Computation Obfuscation by Producing Permutations for Encoding Randomly), to create data which never exists on the device digitally in plaintext format and which is subsequently used for computation. In this paper we utilize COPPER to calculate a moving average computation on encoded data.

embedded systems↗
DOE OSTI2023

Symmetric Random Butterfly Transform (SRBT) Based Preconditioner

Summary of work using Symmetric Random Butterfly Transformation (SRBT) in conjunction with Incomplete LDL T factorization as a preconditioner for FGMRES solver as a way of solving linear systems arising from interior point methods applied to power system problems. These linear systems have proven difficult to parallelize and this represents a possible route forward.

interior point optimization↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2023

The Reduction of Random Uncertainty in Differential Temperature Measurements Using Common Leg Thermocouples

The uncertainty quantification of random error is considered for common leg thermocouples (i.e., where one thermoelement is shared along the length of the TC for all other TC junctions present). The uncertainty is presented for both a common leg and individual, separate leg thermocouples. For Type K thermocouples a reduction in uncertainty by up to 3x is capable when differential temperatures, ?T, are within 150°C, and diminishes to little to no improvement above 150°C.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

Switching current reduction in magnetoresistive random access memories

We present an approach for minimizing the critical current for the magnetization switching in magnetic tunnel junctions by optimizing the spatial distribution of the current density. We show that such a minimization is possible because critical current is determined by the condition of making one of the magnetization eigenstates grow in time. The excitation of the eigenstates is enhanced when the spatial distributions of the eigenstates and current density overlap. Critical current can be viewed as a functional of the current density spatial distribution and it can be minimized by optimizing this distribution. Such an optimization results in a major reduction of the critical current and increase of the switching efficiency, viz. the ratio between the energy barrier and critical current. The minimized critical current increases approximately linearly with the magnetic tunnel junction size, which is much slower than critical current for the case of a uniform current density. The optimized efficiency can be approximately a constant with respect to the magnetic tunnel junction size, which is much higher than the efficiency for the uniform current density. Additional optimization can be achieved by spatially modulating the material parameters, e.g., the saturation magnetization. The presented approach and obtained scaling of the critical current and efficiency offers opportunities for the magnetic tunnel junction optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sub-micrometer random-pyramid texturing of silicon solar wafers with excellent surface passivation and low reflectance

Select emerging solar cell technologies, ranging from very narrow screen-printed fingers to perovskite/silicon tandems, would benefit from a reduction of the height of the standard pyramidal texture of monocrystalline silicon wafers to below 1 μm. However, fabricating such small surface features usually compromises the requisite low reflectance and high passivation quality. Through systematic design of experiments, we demonstrate that conventional alkaline-based wet-chemical etching processes can be tuned to produce dense, spatially homogeneous, and uniformly sized sub-micrometer pyramids. Precise texture size can be further controlled by adding potassium silicate to the texturing solution to effectively slow the pyramid growth rate and promote nucleation. With pyramid size distribution data—extracted from multiple atomic force microscopy images across each wafer—we demonstrate 16 nanotextures with average pyramid sizes ranging from 62 to 512 nm, with the largest having only 0.3% of its pyramids taller than 1 μm. Six of the nanotextures exhibit light trapping and surface defect densities comparable to state-of-the-art microtextured wafers. In conclusion, the best nanotexture achieved an AM1.5G-weighted reflectance of 11.8% and a minority-carrier lifetime of 3.4 ms after passivation with amorphous silicon, demonstrating that smaller textures need not deteriorate solar cell performance.

14 SOLAR ENERGY↗
DOE OSTI2021

Random copolymer of poly(polyethylene glycol methyl ether)methacrylate as tunable transition temperature solid-solid phase change material for thermal energy storage

Polymer based phase change materials (PCM) for thermal energy storage (TES) applications have gained some attention recently due to their high stability and potential solid to solid phase transition. In this work, we are the first to utilize a simple copolymerization strategy for static tunability transition temperature (T t ) of polymeric PCM. The copolymerization between short and long side chain polyethylene glycol based methacrylate polymers was designed to tune T t with minimum impact on their energy density. Polarized optical microscope and x-ray techniques were also used to understand the relationship between crystal structure and T t of different copolymer composition which was discussed in the context. The solid to solid transition polymeric PCM were successfully developed with tunable T t ranged from 18 °C to 35 °C which is suitable toward building envelop applications.

14 SOLAR ENERGY↗
DOE OSTI2023

Quaternary Ammonium-Functionalized Poly(Arylene Ether Sulfone) Random Copolymers for Direct Air Capture

Direct air capture (DAC) is a promising tool for reducing CO 2 concentrations in the atmosphere and fighting climate change. We synthesized a series of quaternary ammonium (QA) functionalized poly(arylene ether sulfone) copolymers with varying mole fractions of charge content and demonstrated their potential as sorbents for DAC. Molecular weight analysis determined high monomer conversion was achieved in <6 h and relatively high molecular weights for all copolymer compositions. Thermal analysis revealed all polymers had degradation temperatures >240 °C and a single glass transition temperature (T g ); the T g decreased with an increase in ion exchange capacity (IEC). Fractional free volume and water uptake percentage increased and water contact angle decreased with the number of QA sites when compared to a neutral, commercially available polysulfone (Udel® P-1700 PSU). Next, the novel series of ammonium-functionalized copolymers exhibited a moisture-mediated capture and release of atmospheric CO 2 . When exposed to air containing 30% relative humidity the polymers captured CO 2 and the capacity increased with the IEC. When the relative humidity was increased to 95% the samples released the CO 2 . This study demonstrates a promising CO 2 DAC sorbent and highlights the possibility for continuous DAC with purpose-built free-standing membranes.

42 ENGINEERING↗
DOE OSTI2023

Construction of a Compact Array of Microplasma Jet Devices and Its Application for Random Mutagenesis of Rhodosporidium toruloides

A small and efficient DNA mutation-inducing machine was constructed with an array of microplasma jet devices (7 × 1) that can be operated at atmospheric pressure for microbial mutagenesis. Using this machine, we report disruption of a plasmid DNA and generation of mutants of an oleaginous yeast Rhodosporidium toruloides. Specifically, a compact-sized microplasma channel (25 × 20 × 2 mm 3 ) capable of generating an electron density of greater than 10 13 cm –3 was constructed to produce reactive species (N 2 *, N 2 +, O, OH, and H α ) under helium atmospheric conditions to induce DNA mutagenesis. The length of microplasma channels in the device played a critical role in augmenting both the volume of plasma and the concentration of reactive species. First, we confirmed that microplasma treatment can linearize a plasmid by creating nicks in vitro. Second, we treated R. toruloides cells with a jet device containing 7 microchannels for 5 min; 94.8% of the treated cells were killed, and 0.44% of surviving cells showed different colony colors as compared to their parental colony. Microplasma-based DNA mutation is energy-efficient and can be a safe alternative for inducing mutations compared to conventional methods using toxic mutagens. In conclusion, this compact and scalable device is amenable for industrial strain improvement involving large-scale mutagenesis.

59 BASIC BIOLOGICAL SCIENCES↗