Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sequential”

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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Microgrid Service Restoration Incorporating Unmonitored Automatic Voltage Controllers and Net Metered Loads

Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Distribution System Blackstart and Restoration Using DERs and Dynamically Formed Microgrids

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids (MGs), sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated MGs. In DERs-aided blackstart, the continuous operation of these MGs is limited by the finite energy capacity of commonly used blackstart units like battery energy storage (BES)-based gridforming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between dynamic MGs and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition is developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottomup blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. In conclusion, the proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Aggregated DER_A Model Parameterization via Online Moving Horizon Estimation

Here, this paper introduces a methodology for parameterizing the DER_A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER_A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodology’s effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER_A model closely mirrors the real system’s voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration.

42 ENGINEERING↗
DOE OSTI2020

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Distributed Inference with Sparse and Quantized Communication

Here, we consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just 1 bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.

42 ENGINEERING↗
DOE OSTI2020

Generation of a highly attenuated strain of Pseudomonas aeruginosa for commercial production of alginate

Alginate is an important polysaccharide that is commonly used as a gelling agent in foods, cosmetics and healthcare products. Currently, all alginate used commercially is extracted from brown seaweed. However, with environmental changes such as increasing ocean temperature and the increasing number of biotechnological uses of alginates with specific properties, there is an emerging need for more reliable and customizable sources of alginate. An alternative to seaweed for alginate production is Pseudomonas aeruginosa, a common Gram-negative bacterium that can form alginate containing biofilms. However, P. aeruginosa is an opportunistic pathogen that can cause life-threatening infections in immunocompromised patients. Therefore, we sought to engineer a non-pathogenic P. aeruginosa strain that is safe for commercial production of alginate. Using a homologous recombination strategy, we sequentially deleted five key pathogenicity genes from the P. aeruginosa chromosome, resulting in the marker-free strain PGN5. Intraperitoneal injection of mice with PGN5 resulted in 0% mortality, while injection with wild-type P. aeruginosa resulted in 95% mortality, providing evidence that the systemic virulence of PGN5 is highly attenuated. Importantly, PGN5 produces large amounts of alginate in response to overexpression of MucE, an activator of alginate biosynthesis. The alginate produced by PGN5 is structurally identical to alginate produced by wildtype P. aeruginosa, indicating that the alginate biosynthetic pathway remains functional in this modified strain. The genetic versatility of P. aeruginosa will allow us to further engineer PGN5 to produce alginates with specific chemical compositions and physical properties to meet different industrial and biomedical needs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Allopregnanolone as an Adjunct Therapy to Midazolam is More Effective Than Midazolam Alone in Suppressing Soman‐Induced Status Epilepticus in Male Rats

ABSTRACT Aims Humans and animals acutely intoxicated with the organophosphate soman can develop sustained status epilepticus (SE) that rapidly becomes refractory to benzodiazepines. We compared the antiseizure efficacy of midazolam, a current standard of care treatment for OP‐induced SE, versus combined therapy with midazolam and allopregnanolone (ALLO) in a rat model of soman‐induced SE. Methods Soman‐intoxicated male rats with robust seizure behavior and high‐amplitude electroencephalographic (EEG) activity were administered midazolam (0.65 mg, i.m.) 20 min after seizure initiation and 10 min later either a second dose of midazolam or ALLO (12 or 24 mg/kg, i.m.). Seizure behavior and EEG were monitored for 4 h after treatment. Brains were collected at the end of the monitoring period for histological analyses. Results Animals receiving 2 doses of midazolam exhibited persistent SE. Sequential dosing with midazolam followed by ALLO suppressed electrographic seizure activity. The combination therapy also significantly reduced soman‐induced neurodegeneration and neuroinflammation compared to 2 doses of midazolam. High but not low dose ALLO was associated with transitory and reversible respiratory compromise during the 1 h period after dosing. Conclusions Treatment with midazolam followed by ALLO was more effective than 2 doses of midazolam in suppressing benzodiazepine‐refractory, soman‐induced SE, and in mitigating its acute neuropathological consequences.

Andrew, Peter M. [Department of Molecular Bioscien↗
DOE OSTI2023

Across‐model spread and shrinking in predicting peatland carbon dynamics under global change

Abstract Large across‐model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across‐model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO 2 levels and five temperature levels. We developed a matrix‐based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter‐model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter‐model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO 2 (eCO 2 ) concentrations in the measurements, simulated NEP responded positively to eCO 2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across‐model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Hou, Enqing↗
DOE OSTI2023

Desorption and Co–Dissolution of Uranium–Bearing Solids During Alkalinity–Enhanced Flushing of Contaminated Sediments

Elevated uranium concentrations in groundwater remain a persistent challenge at contaminated sites. Several sites rely on natural flushing of uranium as a remediation strategy. Uncertainties in conceptual models can cause remediation strategies to underestimate timeframes required to reach remediation goals. In this study, laboratory experiments were conducted to investigate uranium flushing of sediments from a site with persistent groundwater contamination. Six columns were used to simulate alkalinity-enhanced flushing of uranium from sediments, by switching influent alkalinity after ~12 pore volumes. About 20% to 31% of sediment uranium was consistently flushed from the sediments. K d values varied greatly (5.3 to 117 L/kg) but were consistently lower (20% to 50%) during influents with elevated alkalinity. The mass of uranium recovered from the columns also was consistently higher (5% to 80%) during alkalinity-enhanced flushing periods. However, column experiments with sequentially increasing alkalinity showed diminishing returns in uranium elution at higher carbonate concentrations. The loss of flushing efficiency is attributed to significant calcite precipitation at higher alkalinities. Furthermore, the results show that alkalinity-enhanced flushing of uranium could be employed as a viable remediation scheme if calcite precipitation could be minimized in a field application.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Clinical practice gaps and challenges in non‐alcoholic steatohepatitis care: An international physician needs assessment

Abstract Background and aims Even as several pharmacological treatments for non‐alcoholic steatohepatitis (NASH) are in development, the incidence of NASH is increasing on an international scale. We aim to assess clinical practice gaps and challenges of hepatologists and endocrinologists when managing patients with NASH in four countries (Germany/Italy/United Kingdom/United States) to inform educational interventions. Methods A sequential mixed‐method design was used: qualitative semi‐structured interviews followed by quantitative online surveys. Participants were hepatologists and endocrinologists practising in one of the targeted countries. Interview data underwent thematic analysis and survey data were analysed with chi‐square and Kruskal‐Wallis tests. Results Most interviewees ( n = 24) and surveyed participants (89% of n = 224) agreed that primary care must be involved in screening for NASH, yet many faced challenges involving and collaborating with them. Endocrinologists reported low knowledge of which blood markers to use when suspecting NASH (56%), when to order an MRI (65%) or ultrasound/FibroScan® (46%), and reported sub‐optimal skills interpreting alanine aminotransferase (ALT, 37%) and aspartate aminotransferase (AST, 38%) blood marker test results, causing difficulty during diagnosis. Participants believed that more evidence is needed for upcoming therapeutic agents; yet, they reported sub‐optimal knowledge of eligibility criteria for clinical trials. Knowledge and skill gaps when managing comorbidities, as well as skill gaps facilitating patient lifestyle changes were reported. Conclusions Educational interventions are needed to address the knowledge and skill gaps identified and to develop strategies to optimize patient care, which include implementing relevant care pathways, encouraging referrals and testing, and multidisciplinary collaboration, as suggested by the recent Global Consensus statement on NAFLD.

Lazure, Patrice↗
DOE OSTI2020

A bifunctional salvage pathway for two distinct S-adenosylmethionine by-products that is widespread in bacteria, including pathogenic Escherichia coli

S-adenosyl-L-methionine (SAM) is a necessary co-substrate for numerous essential enzymatic reactions including protein and nucleotide methylations, secondary metabolite synthesis, and radical-mediated processes. Radical SAM enzymes produce 5’-deoxyadenosine, and SAM-dependent enzymes for polyamine, neurotransmitter, and quorum sensing compound synthesis produce 5’-methylthioadenosine as byproducts. Both are inhibitory and must be addressed by all cells. This work establishes a bifunctional oxygen-independent salvage pathway for 5’-deoxyadenosine and 5’-methylthioadenosine in both Rhodospirillum rubrum and Extraintestinal Pathogenic Escherichia coli. Homologous genes for this pathway are widespread in bacteria, notably pathogenic strains within several families. A phosphorylase (Rhodospirillum rubrum) or separate nucleoside and kinase (Escherichia coli) followed by an isomerase and aldolase sequentially function to salvage these two wasteful and inhibitory compounds into adenine, dihydroxyacetone phosphate and acetaldehyde or (2-methylthio)acetaldehyde during both aerobic and anaerobic growth. Both SAM byproducts are metabolized with equal affinity during aerobic and anaerobic growth conditions, suggesting that the dual-purpose salvage pathway plays a central role in numerous environments, notably the human body during infection. Finally, our newly discovered bifunctional oxygen-independent pathway, widespread in bacteria, salvages at least two byproducts of SAM-dependent enzymes for carbon and sulfur salvage, contributing to cell growth.

09 BIOMASS FUELS↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Immune activation during Pseudomonas infection causes local cell wall remodeling and alters AGP accumulation

The plant cell boundary generally comprises constituents of the primary and secondary cell wall (CW) that are deposited sequentially during development. Although it is known that the CW acts as a barrier against phytopathogens and undergoes modifications to limit their invasion, the extent, sequence, and requirements of the pathogen-induced modifications of the CW components are still largely unknown, especially at the level of the polysaccharide fraction. To address this significant knowledge gap, we adopted the compatible Pseudomonas syringae–Arabidopsis thaliana system. We found that, despite systemic signaling actuation, Pseudomonas infection leads only to local CW modifications. Furthermore, by utilizing a combination of CW and immune signaling-deficient mutants infected with virulent or non-virulent bacteria, we demonstrated that the pathogen-induced changes in CW polysaccharides depend on the combination of pathogen virulence and the host's ability to mount an immune response. This results in a pathogen-driven accumulation of CW hexoses, such as galactose, and an immune signaling-dependent increase in CW pentoses, mainly arabinose, and xylose. Our analyses of CW changes during disease progression also revealed a distinct spatiotemporal pattern of arabinogalactan protein (AGP) deposition and significant modifications of rhamnogalacturonan sidechains. Furthermore, genetic analyses demonstrated a critical role of AGPs, specifically of the Arabinoxylan Pectin Arabinogalactan Protein1, in limiting pathogen growth. Collectively, our results provide evidence for the actuation of significant remodeling of CW polysaccharides in a compatible host-pathogen interaction, and, by identifying AGPs as critical elements of the CW in plant defense, they pinpoint opportunities to improve plants against diverse pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Identification of glycosyltransferases mediating 2‐ O ‐arabinopyranosyl and 2‐ O ‐galactosyl substitutions of glucuronosyl side chains of xylan

SUMMARY Xylan is one of the major hemicelluloses in plant cell walls and its xylosyl backbone is often decorated at O ‐2 with glucuronic acid (GlcA) and/or methylglucuronic acid (MeGlcA) residues. The GlcA/MeGlcA side chains may be further substituted with 2‐ O ‐arabinopyranose (Ara p ) or 2‐ O ‐galactopyranose (Gal) residues in some plant species, but the enzymes responsible for these substitutions remain unknown. During our endeavor to investigate the enzymatic activities of Arabidopsis MUR3‐clade members of the GT47 glycosyltransferase family, we found that one of them was able to transfer Ara p from UDP‐Ara p onto O ‐2 of GlcA side chains of xylan, and thus it was named xylan 2‐ O ‐arabinopyranosyltransferase 1 (AtXAPT1). The function of AtXAPT1 was verified in planta by its T‐DNA knockout mutation showing a loss of the Ara p substitution on xylan GlcA side chains. Further biochemical characterization of XAPT close homologs from other plant species demonstrated that while the poplar ones had the same catalytic activity as AtXAPT1, those from Eucalyptus , lemon‐scented gum, sea apple, 'Ohi'a lehua, duckweed and purple yam were capable of catalyzing both 2‐ O ‐Ara p and 2‐ O ‐Gal substitutions of xylan GlcA side chains albeit with differential activities. Sequential reactions with XAPTs and glucuronoxylan methyltransferase 3 (GXM3) showed that XAPTs acted poorly on MeGlcA side chains, whereas GXM3 could efficiently methylate arabinosylated or galactosylated GlcA side chains of xylan. Furthermore, molecular docking and site‐directed mutagenesis analyses of Eucalyptus XAPT1 revealed critical roles of several amino acid residues at the putative active site in its activity. Together, these findings establish that XAPTs residing in the MUR3 clade of family GT47 are responsible for 2‐ O ‐arabinopyranosylation and 2‐ O ‐galactosylation of GlcA side chains of xylan.

Zhong, Ruiqin↗
DOE OSTI2021

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

Engineering↗
DOE OSTI2021

Numerical Analysis of Fuel Effects on Advanced Compression Ignition Using a Cooperative Fuel Research Engine Computational Fluid Dynamics Model

Growing environmental concerns and demand for a better fuel economy are driving forces that motivate the research for more advanced engines. Multi-mode combustion strategies have gained attention for their potential to provide high thermal efficiency and low emissions for light-duty applications. These strategies target optimizing the engine performance by correlating different combustion modes to load operating conditions. The extension from boosted spark ignition (SI) mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing the compression ratio and utilizing intake air heating. Further, in order to enable an accurate control of intake charge condition for ACI mode and rapid mode-switches, it is essential to gain fundamental insights into the autoignition process. Within the scope of ACI, homogeneous charge compression ignition (HCCI) mode is of significant interest. It is known for its potential benefits, operation at low fuel consumption, low NOx, and particulate matter (PM) emissions. In the present work, a virtual Cooperative Fuel Research (CFR) engine model is used to analyze fuel effects on ACI combustion. In particular, the effect of fuel octane sensitivity (S) (at constant Research Octane Number (RON)) on autoignition propensity is assessed under beyond-RON (BRON) and beyond-MON (BMON) ACI conditions. The three-dimensional CFR engine computational fluid dynamics (CFD) model employs a finite-rate chemistry approach with a multi-zone binning strategy to capture autoignition. Two binary blends with Research Octane Number (RON) of 90 are chosen for this study: primary reference fuel (PRF) with S = 0 and toluene–heptane (TH) blend with S = 10.8, representing paraffinic and aromatic gasoline surrogates. Reduced mechanisms for these blends are generated from a detailed gasoline surrogate kinetic mechanism. Simulation results with the reduced mechanisms are validated against experimental data from an in-house CFR engine, with respect to in-cylinder pressure, heat release rate, and combustion phasing. Thereafter, the sensitivity of combustion behavior to ACI operating condition (BRON versus BMON), air-fuel ratio (λ = 2 and 3), and engine speed (600 and 900 rpm) is analyzed for both fuels. It is shown that the sensitivity of a fuel’s autoignition characteristics to λ and engine speed significantly differs at BRON and BMON conditions. Moreover, this sensitivity is found to vary among fuels, despite the same RON. It is also observed that the presence of low-temperature heat release (LTHR) under BRON condition leads to more sequential autoignition and longer combustion duration than BMON condition. Finally, the study indicates that the octane index (OI) fails to capture the trend in the variation of autoignition propensity with S under the BMON condition.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗