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At least 379 records · Page 21

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

Schulte, Jan-Frederik [Purdue U.] (ORCID:000000034↗

Design and Testing of the Endcap Concentrator ASICs for the CMS High-Granularity Calorimeter Upgrade

A major upgrade of the High-Granularity Calorimeter (HGCAL) in the CMS detector is planned for Long-Shutdown 3 (LS3), currently expected to start in 2026. This upgrade will contain over 6 million channels and the electronics will be required to be low power and to withstand a radiation environment with a High-Energy Hadron flux of 3x10**6 particles per square centimeter per second. The solution to this significant challenge is the two Endcap Concentrator (ECON) ASICs, ECON-T and ECON-D, working in tandem with the HGCROC front-end ASIC. The two ECON ASICs provide critical on-detector data reduction for both the 40 MHz trigger path (ECON-T) and 750 kHz data acquisition path (ECON-D) of the HGCAL. The ASICs are fabricated in 65nm CMOS. They are rad-tolerant to 600 Mrad with low power consumption (<2.5 mW/channel). This presentation will be a comprehensive description of each ECON design, including the infrastructure that they share as well as the elements that make each unique. The presentation will also include functionality and radiation tests for both ASICs, and the first high statistics characterization results from the full production of 75k ECON-D and ECON-T ASICs.

Hoff, James R. [Fermilab] (ORCID:0000000163514592)↗

Variable Frequency Drive (VFD): Emphasizing Energy Savings and Efficiency in Industrial Applications

This project explores the energy saving potential of using a VDA in an industrial pumping application. A 24-horsepower pump, 3-phase centrifugal pump powered by 480V AC supply analyzed under variable frequency control using an ABB ACS880-31-052A-5 VFD. by modulating the motor speed between 30Hz and 60Hz, the VFD enables precise control of power consumption based on system set speed. The article is intended for public cause of the poster session and presentation session. INL/Exp-25-86570

99 - GENERAL AND MISCELLANEOUS↗

Passive Energy-Saving Solutions for Clothes Dryers: A Modeling and Experimental Study for Improved Efficiency and Affordability

Dryers are integral appliances in modern households, yet their significant power consumption remains a critical challenge for reducing energy bills and upgrades. This work presents the outcomes of a comprehensive investigation aimed at lowering the operating energy costs of the dryer through modeling and experimental approaches. Specifically, the objectives of this research are twofold: (1) to reduce energy consumption without negatively impacting drying performance or time; and (2) to ensure affordability by developing retrofittable solutions characterized by low cost and a quick payback period. These strategies promise universal applicability to all dryer categories, encompassing both gas and electric models, by tackling core issues such as unnecessary heat loss and excess energy supply, both prevalent across dryer designs. Our methodology combines robust modeling frameworks and experimental validation to target energy efficiency improvements through three primary pathways: (1) effective heat loss management using ultralow-cost insulation materials tailored for dryer systems; (2) heat management across the drying cycle, facilitated by passive heat transfer mechanisms, and (3) optimization of heat supply to the load to ensure precise energy delivery, minimizing waste. These innovations are designed to seamlessly integrate with existing dryer configurations, providing a scalable and retrofittable solution that ensures affordability without requiring substantial redesigns or expensive components. Using these methods, the study demonstrates improvement opportunities in dryer energy consumption while maintaining the desired performance. The modeling component utilizes computational simulations to evaluate the thermal and energy performance of these innovations under varied operational conditions, providing foundational insights for experimental design. By addressing critical areas such as heat loss, energy recovery, and supply optimization, this work proposes impactful solutions for lowering the energy costs in domestic clothes dryers. The ultralow-cost, retrofittable nature of the proposed strategies ensures widespread adoption potential across diverse dryer categories, making significant strides toward affordable household energy practices.

Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗

Case Study of Integrating High-Temperature Heat Pump with LiBr-H2O Absorption Chiller for Data Center Liquid Cooling

Data centers (DCs) are physical infrastructures that support artificial intelligence workloads. The rapid growth of artificial intelligence is putting substantial pressure on the US power grid. Most of electricity consumed by IT equipment, accounting for 50%-60% of total DC power, ultimately becomes waste heat. This heat is dissipated by DC’s cooling facilities, accounting for an additional 30%-40% of total DC power. Recovering and repurposing this waste heat offers a significant opportunity to enhance energy efficiency and reduce operating costs of DCs. One potential pathway is converting heat to cold using thermal-driven absorption chillers, therefore, reducing the power consumption in DC cooling facilities. Existing studies mainly demonstrate the technical and economic feasibility of repurposing DC’s waste heat for cooling applications but provide limited technical details on how to integrate the thermal-driven absorption chillers with DC cooling systems. In addition, the low-grade waste heat available from DCs must be upgraded to higher temperatures suitable for absorption chillers. This paper presents a case study on integrating high-temperature heat pumps with a LiBr-H2O absorption chiller to use DC waste heat for cooling. A thermodynamic model of single-effect, LiBr-H2O absorption chiller and an empirical model of high-temperature heat pumps were built. The case study considers ASHRAE W17 liquid-cooled DC, with facility service water supplied at 17.0℃ and returned at 25.3℃. The thermal behaviors of absorption chiller components were predicted for the generation temperature ranging from 75.0℃ to 115.0℃. Based on the available waste heat in the integrated system, two waste heat recovery strategies were evaluated: a facility service water-based strategy and cooling water-based strategy. Results indicated that the cooling water-based strategy achieves higher Coefficient of Performance (COPs) than the facility service water-based strategy. The relatively low cooling COPs of single-effect LiBr-H2O absorption chillers could be offset by high heating COP of high temperature heat pumps. The maximum cooling COP of absorption chiller and the overall COP of integrated systems occur at lower generation temperatures, but these conditions also yield lower cooling capacities. In practice, system operation should balance the trade-off between the COP and cooling capacity

Wang, Pengtao [ORNL] (ORCID:0000000214713429)↗

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗

Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer

In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.

Wojda, Rafal↗

A techno-economic evaluation of solar-powered green hydrogen production for sustainable energy consumption in Belgium

Here this study focuses on the production and consumption of solar-powered green hydrogen in Belgium by comparing the costs of domestic production with those of imports from Morocco and Namibia. The levelized cost of hydrogen (LCOH) is determined via a techno-economic assessment considering various parameters including, power connectivity, electrolyzer types, location factors, and risk premium. The results show that the lowest LCOH can be achieved by on-grid Proton exchange membrane electrolyzers (PEMEL). LCOH of production in Belgium, Morocco, and Namibia with a PEMEL on-grid system are 5.64, 6.32 and 5.13 euro/kg H 2 , respectively. Thus, importing hydrogen from Namibia to Belgium is more competitive than domestic production. The sensitivity analysis suggests that a target LCOH of 3 euro/kg H 2 is achievable by importing from Namibia through various cost combinations. One of these combinations involves reducing the cost of PV panels to 222.3 euro/kW (70% reduction) and electrolyzers to 363.3 euro/kW (25% reduction).

08 HYDROGEN↗

Tunneling Barrier-Integrated Gold Nanofilms for Negative Strain Gauging with Near-Zero Energy Consumption

Wireless strain sensors with minimal power needs are essential for long-term monitoring in energy-limited environments. We present a soft tunneling barrier-integrated gold thin film for negative strain sensing with near-zero energy consumption. The device features a strain-induced transition from an insulating to a metallic state, increasing conductivity by 9 orders of magnitude under a controlled strain. It consists of Au-PDMS-Au nanofilm layers, where the Au structures are near the percolation threshold and the PDMS layer acts as a tunneling barrier. Under strain, thinning due to the Poisson effect lowers the barrier’s potential height, enabling electron tunneling and forming an electrical path. Further, with a standby power consumption of ~10 –5 mW over 10 6 times lower than conventional sensors (~12.5 mW), this device is ideal for real-time, long-term stationary structural monitoring in multiple locations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Guest Editorial Special Issue on Emerging Topics of Power Electronics Interfaced Battery Energy Storage System

No doubt, battery energy storage systems have been the enabling solution to balance generation and consumption of power systems with high-penetration renewable energy resources, long-range electric vehicles, and various smart devices. Upon the battery has been manufactured, the rest of implementation issues become how to interface battery and related systems, how to regulate the charging/discharging power, and how to keep battery energy storage systems (BESSs) safe, reliable, and long-term service. In this editorial, we discuss how power electronic converters and associated control and optimization, which can maximize the value of BESSs, are devoted to provide cost-effective, efficient, and even revolutionary technologies.

25 ENERGY STORAGE↗

AstroPix4 — a novel HV-CMOS sensor developed for space based experiments

For the proposed space based gamma-ray observatory All-sky Medium-Energy Gamma-ray Observatory eXplorer (AMEGO-X), a silicon tracker based on a novel High Voltage-CMOS (HV-CMOS) sensor called AstroPix, is currently being developed. Preliminary measurements with the first full reticle prototype AstroPix3 show that the power target of 1.5 mW/cm 2 can currently not be reached due to the digital consumption of 3.08 mW/cm 2 , while the analog power consumption of 1.04 mW/cm 2 and a break down voltage of over 350 V look promising. Based on these results, the design changes in AstroPix4, submitted in May 2023, are presented, containing changes to the time stamp generation and readout architecture. A digital power consumption below 0.25 mW/cm 2 is expected by removing the fast 200 MHz clock used to measure the time-over-threshold (ToT) and an LVDS receiver. A maximum resolution of 3.125 ns for time-of-arrival (ToA) and ToT is reached by adding per-pixel Flash-Time-to-Digital Converter (TDCs) controlled by a global delay-locked loop (DLL).

47 OTHER INSTRUMENTATION↗

BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

Array↗

Reactive Carbon Capture: Cooperative and Bifunctional Adsorbent-Catalyst Materials and Process Integration for a New Carbon Economy

To say the least, releasing CO 2 into the atmosphere is reaping undue environmental consequences given the ever-present increase in severe global weather events over the past five years. However, it can be argued that–at least in the confines of current technological capabilities–the atmospheric release of CO 2 is somewhat unavoidable given that even shifting toward clean energy sources–such as solar, nuclear, wind, battery, or H 2 power–incurs an initial carbon requirement by way of manufacturing the very production abilities through which “clean” energy is generated. Even years from now, experts agree that energy production will be diversified and–as the global population continues to drive the growth of global energy consumption–thermal power derived from carbon combustion is likely to remain one intrinsic energetic source, of which CO 2 will always be a byproduct. In this context, it is the responsibility of the scientific community to devise improved pathways of carbon management such that (i) the consequences of combustion on the global environment are reduced and (ii) carbon fuels can be leveraged in a sustainable fashion. In this Account, we discuss a pivotal perspective shift on CO 2 emissions derived from a considerable breakthrough in material science from our work on shape engineering of nanoporous adsorbents and catalysts. This account details the development of materials which no longer vilify CO 2 emissions as a valueless combustion byproduct, instead providing a path for them to become a potential feedstock. In more specific terms, this work details the development of structured, cooperative “bifunctional” materials (BFMs) comprised of (i) a high-temperature adsorbent and (ii) a heterogeneous catalyst that enable single-bed CO 2 capture and utilization in oxidative ethane dehydrogenation (ODHE), oxidative propane dehydrogenation (ODHP), and dry methane reforming (DMR) processes. This Account begins with the conceptual development of the BFMs in the powdered state, followed by detailing the first-ever reports of structuring the materials into facile honeycomb contactors by 3D printing. The Account then summarizes the impressive performance of the 3D-printed BFMs, specifically focusing on how their catalysts (metal oxides and perovskites) influence their reactive CO 2 capture performances in ODHE, ODHP, and DMR processes. Such promise of CO 2 -as-fuel offers a glimpse into the future of a diversified energy economy, in which CO 2 /fuel looping can play an important role. A major factor in achieving this future is, of course, developing an appropriately active catalyst; an account of whose first breakthroughs in material science are detailed herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Grid-Forming Wind: Getting Ready for Prime Time With and Without Inverters

Variable wind power is one of the fastest-growing energy-generation technologies, harnessing the energy of wind, both on land and at sea. During the past decade, the global share of wind power has grown tremendously, and wind power is evolving into a major contributor to electricity supplies in many countries. In this journey, wind is also becoming a source of reliability services to the grid, which has required grid-supporting functions originally provided by synchronous generators, enabling very high levels of instantaneous penetration (ranging from 60 to 70% in some power systems). To get beyond this, a fundamental shift is required to address challenges associated with the transition to a grid with only a few remaining (or even without any) conventional synchronous generators while achieving a minimum acceptable level of stability.

energy consumption↗

Coordinated Modeling of Electric Grid and Natural Gas Network Operations

Presentation based upon full report of a Colorado case study and coordination framework, which is available at https://www.nrel.gov/docs/fy20osti/77096.pdf. JISEA analysts Brian Sergi, Omar Guerra, and Bri-Mathias Hodge will present on recent JISEA work on coordination between the natural gas and electricity sectors. Power and gas are becoming increasingly interdependent but weren't designed to function together. How does greater coordination impact system operations with different levels of solar and wind penetrations? This free JISEA presentation will take place in a Webinar on Tuesday, May 11, 2021 at 12 p.m.: Learn how greater coordination between the natural gas and electricity sectors impacts system operations with different levels of solar and wind penetrations. Presentation information includes: Historical and projected data for natural gas consumption and power generation in the United States as demonstrated by 2014 East Coast Polar Vortex, 2021 Texas Winter Storm Uri. Showing coupling points, FERC identified need for better coordination, different levels of coordination (decision making and optimization control), coordination framework. Case study on Colorado Front Range, ramping requirements and gas nominations, real-time dispatch (June and December scenarios), results of analysis of impacts of coordination on unserved load. Total real-time gas offtakes by node, impacts on unserved gas, effect on CO2 emissions. Discussions on clusions drawn from Colorado Case study expanded co-simulation via HELICS, objectives of the HELICS+ natural gas use case, modeling of hydrogen blending, techno economic assessment of blending, and blending impacts on energy content and pressure.

analysis↗

Effects-Based Monitoring of Geomagnetically-Induced Current Using a Convolutional Neural Network

Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. Finally, the results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

42 ENGINEERING↗

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗