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DOE OSTI2021

Methodology for interpretable reinforcement learning model for HVAC energy control

Deep reinforcement learning (DRL) approaches have been used in various application areas to improve efficiency, optimization, or automation. However, very little is known about how the DRL algorithms make decisions and what features affect their performance. Using a case study of a DRL based Heating, Ventilation and Air Conditioning (HVAC) optimization methodology, we demonstrate how we can address these challenges by applying interpretability tools and systematically exploring the model inputs for better understanding the DRL behaviour and decision making process. We developed a methodology for interpretable reinforcement learning and evaluated our approach in real-world house located in Knoxville, TN. Our findings explain the reasoning behind DRL-based optimization decisions under different circumstances which has been discussed and confirmed by the experts in the field.

Kotevska, Olivera↗

DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗

DOE OSTI2020

Design of Auxiliary Circuit Elements for Achieving Zero Voltage Switching in a Wireless Power Transfer System

The auxiliary resonant commutated pole (ARCP) is being used to achieve zero voltage switching (ZVS) of power switches for improved efficiency and/or to reduce dv/dt at the switch node for better electromagnetic interference (EMI) performance and overall system reliability. This paper shows how an ARCP circuit can be used in a wireless power transfer system to achieve ZVS. Detailed analysis of ARCP circuit operation is presented to determine the requirements for achieving ZVS in the main switches of a wireless electric vehicle charging system. Based on this analysis, a design algorithm is developed to optimize the design of the ZVS assisting circuit components. The designed ARCP achieves soft switching of the MOSFETs in the primary inverter of an 85 kHz, 10 kW series-series compensated, stationary wireless power transfer system.

Saha, Tarak↗

DOE OSTI2023

An Intelligent Metasurface-Based Wireless Power Transfer System

Wireless Power Transfer (WPT) systems are getting more popular in recent years in a wide range of application areas. However, the performance of wireless power transfer systems is adversely affected by coil misalignment and increased power transfer distance. To overcome the limitations, the proposed method in this paper will place an intelligent controllable metasurface between the transmitter and receiver coils. The goal is to establish the feasibility of a metasurface-assisted, intelligent WPT charging system by variable coil design and angle of arrival estimation using machine learning for multiple misalignment cases. We will feed these angles of arrival data to reconfigure the metasurface. The inclusion of a controllable metasurface will improve efficiency and increase the power transfer distance.

Aydin, Emrullah↗

DOE OSTI2024

Preparing an on-Demand Cloud Processing Workflow for NISAR Ecosystems Science Products

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance.

Christensen, Alexandra↗

DOE OSTI2023

Design and Optimization of a Fast-Switching Electromagnetic Valve Actuator for Off-highway Vehicle Hydraulic Systems

Electrification of off-highway vehicles offers improved efficiency and reduction in greenhouse gas emissions. However, the extremely low power densities of electric machines relative to the hydraulics used in conventional off-highway vehicles makes electrification challenging. Although the recently proposed Hybrid Hydraulic Electric Architecture (HHEA) can overcome this limitation, it requires frequent switching between the pressure rails using switching valves resulting in losses. Fast switching minimizes the switching losses in these valves, which otherwise significantly reduce the overall efficiency of the system. The primary contribution of this paper is the design and optimization of a linear electromagnetic actuator for a fast-switching valve. This paper identifies the actuator requirements to enable fast switching and presents a design and optimization technique to realize actuators that meet the design objectives. In addition, a multi-physics approach is proposed to model the performance of the complete switching valve by considering the electromagnetic, fluidic, and mechanical physics. An optimal actuator design is obtained using the developed optimization framework, and the multi-physics model demonstrates that the selected design achieves a 30% reduction in throttling-related switching losses by switching 70 % faster than comparable commercial valve designs.

Linear actuators, off-highway vehicle electrificat↗

DOE OSTI2026

A Dual-Active-Bridge Converter Employing a Variable Inductor Without an Auxiliary Circuit

This paper proposes a dual active bridge (DAB) converter employing a variable inductor (VI) without an auxiliary circuit. Unlike conventional VI-based designs that require an external DC bias circuit, the proposed DAB utilizes the input DC current itself as the bias source, enabling automatic inductance variation with load conditions. The inductance naturally increases at low power and decreases at high power, effectively extending the zero voltage switching (ZVS) range and reducing circulating current, respectively. The VI was experimentally implemented and characterized to obtain its inductance-current profile, which was then integrated into a PLECS model of the DAB converter for circuit and thermal simulations. Simulation results confirm that the proposed auxiliary-free VI-DAB converter achieves a wider ZVS range and lower circulating current compared with a conventional fixed-inductor DAB converter. By realizing a variable inductor without any auxiliary bias circuitry, the proposed approach maintains soft switching and reduces reactive current losses across a wide load range, leading to improved efficiency and simplified implementation.

Jo, Cheolhui [ORNL] (ORCID:0000000322692434)↗

DOE OSTI2021

Optimal Control of the Energy-Saving Hybrid Hydraulic-Electric Architecture (HHEA) for Off-Highway Mobile Machines

Most off-highway constructions and agriculture equipment use hydraulics, which has unmatched power density, for power transmission and throttling as a means for control. A novel hybrid hydraulic-electric architecture (HHEA) has recently been proposed to improve efficiency for high-power machines that would have been cost-prohibitive to electrify directly. HHEA uses a set of common pressure rails (CPRs) to transmit the majority of power hydraulically and small electric motor drives to modulate that power and to achieve precise control. This article proposes a computationally efficient Lagrange multiplier method (LMM) for computing the optimal sequence of pressure rail selections to minimize energy use. This is needed to evaluate HHEA's energy-saving potential and for iterative architecture design and sizing. An interesting complication is that the cost function is not fully defined until the candidate control sequence is fully specified. This issue is dealt with by decomposing the original problem into a set of sub-problems with additional constraints that can be solved efficiently. Computational effort can be further reduced if actuators are optimized individually instead of together. However, additional steps are required to prevent the constraint functions from becoming discontinuous with respect to the Lagrange multipliers, which is necessary for meeting the constraints. Lastly, a case study of a construction machine demonstrates the efficacy of the method and shows that the HHEA reduces energy consumption by 68%-73% compared to the baseline load-sensing architecture.

Lagrange multiplier↗

DOE OSTI2022

Multiport Control with Partial Power Processing in Solid-State Transformer for PV, Storage, and Fast-Charging Electric Vehicle Integration

This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage DC sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared to voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional AC/AC, AC/DC, or DC/DC conversion using an inductive DC link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced DC-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching solid-state transformer (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. Here, the DC-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware.

14 SOLAR ENERGY↗

DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗

DOE OSTI2021

Gasoline Compression Ignition on a Light-Duty Multi-Cylinder Engine Using a Wide Range of Fuel Reactivities and Heavy Fuel Stratification

Many research studies have focused on utilizing gasoline in modern compression ignition engines to reduce emissions and improve efficiency. Collectively, this combustion mode has become kn+own as gasoline compression ignition (GCI). One of the biggest challenges with GCI operation is maintaining control over the combustion process through the fuel injection strategy, such that the engine can be controlled on a cycle-by-cycle basis. Research studies have investigated a wide variety of GCI injection strategies (i.e., fuel stratification levels) to maintain control over the heat release rate while achieving low-temperature combustion (LTC). This work shows that at loads relevant to light-duty engines, partial fuel stratification (PFS) with gasoline provides very little controllability over the timing of combustion. On the contrary, heavy fuel stratification (HFS) provides very linear and pronounced control over the timing of combustion. However, the HFS strategy has challenges achieving LTC operation due to the air handling burdens associated with the high exhaust gas recirculation (EGR) rates that are required to reduce NOx emissions to near zero levels. In this work, a wide variety of gasoline fuel reactivities (octane numbers ranging from <40 to 87) were investigated to understand the engine performance and emissions of HFS-GCI operation on a multi-cylinder light-duty engine. Overall, the results indicate that over an EGR sweep at 4 bar brake mean effective pressure (BMEP), the gasoline fuels can achieve LTC operation with ultra-low NO x and soot emissions, while conventional diesel combustion (CDC) is unable to simultaneously achieve low NO x and soot. At 10 bar BMEP, all the gasoline fuels were compared to diesel, but using mixing controlled combustion and not LTC.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part II: The Effect of Roughness on the Performance of a High-Pressure Turbine Blade

Abstract Turbine blades generally present surface roughness introduced in the manufacturing process or caused by in-service degradation, which can have a significant impact on aero-thermal performance. A better understanding of the fundamental physical mechanisms arising from the interaction between the roughness and the turbine flow at engine-relevant conditions can provide insights for the design of blades with improved efficiency and longer operational life. To this end, a high-fidelity numerical framework combining a well-validated solver for direct numerical simulation and a second-order accurate immersed boundary method is employed to predict roughness-induced aero-thermal effects on an LS89 high-pressure turbine (HPT) blade at engine-relevant conditions. Different amplitudes and distributions of surface roughness are investigated and a reference smooth-blade simulation under the same flow conditions is conducted for comparison. Roughness of increasing amplitude progressively shifts the blade suction side boundary layer transition upstream, producing larger values of the turbulent kinetic energy and higher total wake losses. The on-surface data-capturing capabilities of the numerical framework provide direct measurements of the heat flux and the skin friction coefficient, hence offering quantitative information between the surface topology and engineering-relevant performance parameters. This work may provide a benchmark for future numerical studies of turbomachinery flows with roughness.

Engineering↗

DOE OSTI2024

Understanding Environmental Barrier Coating Lifetimes and Performance for Industrial Gas Turbines

Hydrogen or hydrogen blend fuels are expected to replace natural gas in land-based industrial gas turbines (IGTs) to support a greener power economy. Silicon carbide (SiC) base ceramic matrix composites (CMCs) are considered for replacement of Ni-based superalloys to facilitate future efficiency improvements. SiC CMCs require environmental barrier coatings (EBCs) to mitigate volatilization from high-temperature steam, thus making the EBC lifetime critical information for identifying CMC component lifetimes. Here, the goal of this project is to determine the maximum bond coating temperature underneath the EBC for achieving an IGT component lifetime goal of 25,000 h, which is far greater than current CMC component lifetime requirements for aeroturbine applications. To provide data for the lifetime model, laboratory testing used atmospheric plasma-sprayed rare-earth silicate EBCs on monolithic SiC substrates with an intermediate Si bond coating. Specimens exposed to 1-h thermal cycles in flowing air–steam environments and reaction kinetics were assessed from 700 °C to 1350 °C by measuring the thickness of the thermally grown silica scales. The silica growth and phase transformation appear critical in predicting EBC lifetime and several strategies have been explored to reduce the oxide growth rate and improve EBC durability at elevated temperatures. Advanced characterization using Raman spectroscopy has helped clarify this system.

08 HYDROGEN↗

DOE OSTI2025

Unsupervised Image-Based Classification of Corrosion Severity in Automobile Engine Connecting Rods

Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.

42 ENGINEERING↗

DOE OSTI2024

Understanding EBC Lifetimes and Performance for Industrial Gas Turbines

Hydrogen or hydrogen blend fuels are expected to replace natural gas in land-based industrial gas turbines (IGTs) to support a greener power economy. Silicon carbide (SiC) base ceramic matrix composites (CMCs) are considered for replacement of Ni-based superalloys to facilitate future efficiency improvements. SiC CMCs require environmental barrier coatings (EBCs) to mitigate volatilization from high-temperature steam, thus making the EBC lifetime critical information for identifying CMC component lifetimes. The goal of this project is to determine the maximum bond coating temperature underneath the EBC for achieving an IGT component lifetime goal of 25,000 h, which is far greater than current CMC component lifetime requirements for aero-turbine applications. To provide data for the lifetime model, laboratory testing used plasma-sprayed rare-earth silicate EBCs on monolithic SiC substrates with an intermediate Si bond coating. Specimens exposed to 1-h thermal cycles in flowing air-steam environments and reaction kinetics were assessed from 700°-1350°C by measuring the thickness of the thermally grown silica scales. The silica growth and phase transformation appear critical in predicting EBC lifetime and several strategies have been explored to reduce the oxide growth rate and improve EBC durability at elevated temperatures. Advanced characterization using Raman spectroscopy has helped clarify this system.

Ridley, Mackenzie↗

DOE OSTI2020

Numerical study of advanced compression ignition and combustion in a gasoline direct injection engine

Multi-mode combustion strategies may provide a considerable thermal efficiency improvement targeted at part-load operating conditions for light-duty spark-ignition (SI) engines. The extension from boosted SI mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing compression ratio and utilizing intake air heating. In order to enable an accurate control of intake charge condition for ACI control and rapid mode-switches, it is essential to gain fundamental insight into the autoignition process with regard to thermal and fuel-air mixture stratification in the combustion chamber. In this work, a computational fluid dynamics (CFD) study is carried out to reveal some degrees of correlation between the mixture and thermal stratifications induced by the cylinder wall temperature and combustion-phasing across varying engine load conditions. The computational analysis begins with a calibrated simulation setup best matching the engine experiments, and subsequently evaluates the baseline setup for an extended range of engine load conditions for two excess-air ratio cases. The present study emphasizes the dominance of thermal stratifications for autoignition process due to wall temperature effects depending on the engine load conditions of interest. In addition, this study also aims to provide insight into the impact of mixture stratification on cyclic variability, especially at colder wall engine conditions (e.g., cold start). Observations herein provide highlight of the propensity of autoignition in correlation with mixture reactivity space.

Advanced Compression Ignition (ACI) engine↗

DOE OSTI2020

Gasoline Compression Ignition (GCI) on a Light-Duty Multi-Cylinder Engine Using a Wide Range of Fuel Reactivities and Heavy Fuel Stratification

Many research studies have focused on utilizing gasoline in modern compression ignition engines to reduce emissions and improve efficiency. Collectively, this combustion mode has become known as gasoline compression ignition (GCI). One of the biggest challenges with GCI operation is maintaining control over the combustion process through the fuel injection strategy, such that the engine can be controlled on a cycle-by-cycle basis. Research studies have investigated a wide variety of GCI injection strategies (i.e., fuel stratification levels) to maintain control over the heat release rate while achieving low temperature combustion (LTC). This work shows that at loads relevant to light-duty engines, partial fuel stratification (PFS) with gasoline provides very little controllability over the timing of combustion. On the contrary, heavy fuel stratification (HFS) provides very linear and pronounced control over the timing of combustion. However, the HFS strategy has challenges achieving LTC operation due to the air handling burdens associated with the high EGR rates that are required to reduce NOx emissions to near zero levels. In this work, a wide variety of gasoline fuel reactivities (octane numbers ranging from < 40 to 87) were investigated to understand the engine performance and emissions of HFS-GCI operation on a multi-cylinder light-duty engine. The results indicate that over an EGR sweep at 4 bar BMEP, the gasoline fuels can achieve LTC operation with ultra-low NOx and soot emissions, while conventional diesel combustion (CDC) is unable to simultaneously achieve low NOx and soot. At 10 bar BMEP, all the gasoline fuels were compared to diesel, but using mixing controlled combustion and not LTC.

Dempsey, Adam↗

DOE OSTI2024

Learning Functions Varying along a Central Subspace

Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of an s-Hölder function in $R^D$ which varies along a central subspace of dimension $d$ while $d \ll D$. A direct approximation of $f$ in $R^D$ with an accuracy $\varepsilon$ requires the number of samples in the order of $\varepsilon^{-(2s+D)/s}$. In this paper, we analyze the generalized contour regression (GCR) algorithm for the estimation of the central subspace and use piecewise polynomials for function approximation. GCR is among the best estimators for the central subspace, but its sample complexity is an open question. In this paper, we partially answer this questions by proving that if a variance quantity is exactly known, GCR leads to a mean squared estimation error of $O(n^{-1})$ for the central subspace. The estimation error of this variance quantity is also given in this paper. The mean squared regression error of $f$ is proved to be in the order of $(n/\log n)^{-\frac{2s}{2s+d}}$, where the exponent depends on the dimension of the central subspace instead of the ambient space . This result demonstrates that GCR is effective in learning the low-dimensional central subspace. We also propose a modified GCR with improved efficiency. Here, the convergence rate is validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗