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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.

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At least 19 records

A 194nW Energy-Performance-Aware loT SoC Employing a 5.2nW 92.6% Peak Efficiency Power Management Unit for System Performance Scaling, Fast DVFS and Energy Minimization

A self-powered IoT system-on-chip (SoC) reduces power to sub-μw and employs multiple power-management techniques to trade-off ultra-low power (ULP), higher performance, smaller energy harvester footprint, and longer operating lifetime. Minimum Energy Point Tracking (MEPT) [1]–[4] keeps an SoC operating at the minimum energy point (MEP) to enhance system lifetime. Previous sample-and-hold MEPT schemes need frequent voltage comparisons and a high-frequency clock that increases power [2]. Current-ratio-based MEPT relies on specialized CMOS technology for body-bias tuning [3]. A switched-capacitor-based MEPT can achieve energy minimization at a targeted performance [4], but it uses a 30MHz clock witμW power consumption and low power efficiency. For ULP IoT applications, SoCs need to have ultra-low quiescent power, high efficiency for energy delivery, performance scaling based on available energy, and energy minimization to increase system lifetime. In this work, we propose an ULP IoT SoC with a triple-mode power management unit (PMU) that integrates energy-performance scaling, event-driven fast DVFS, and MEPT features to improve the system energy efficiency, as shown in Fig. 13.8.1. This work achieves a minimum 194nW power consumption for the SoC and 5.2nW quiescent power for the PMU with a 92.6% peak efficiency and >10 4 dynamic range. The timing waveform in Fig. 13.8.1 (bottom), demonstrates the transition of the three modes including energy aware (EA), performance aware (PA), and MEPT based on event priority and input voltage level which reflects the energy availability. As such, the system energy consumption and performance could be well-balanced based on both the input and output conditions.

self-powered IoT system-on-chip (SoC)↗

Defining Power Efficient Residential Electric Appliances and Equipment

While the long-established concept of "energy efficiency" has successfully reduced energy consumption and bills in homes, it is no longer sufficient to address the modern challenges of electrical load growth, infrastructure upgrades and associated affordability challenges. We introduce the concept of “power efficiency” for household appliances and equipment, providing a framework to support affordable home modernization and grid management. Energy efficiency is the measure of energy used (kWh) to provide comfort/conveniences, power efficiency is the measure of power used (kW) to provide those same comforts/conveniences. Energy efficiency was needed because energy is a limited resource and expensive. Now power efficiency is needed because power delivery into a building is a limited resource and expensive. We propose a new set of metrics for defining and measuring power efficiency by investigating how current appliance nameplate ratings are established, and assessing the availability and use of existing data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

New bipolar host materials for high power efficiency green thermally activated delayed fluorescence $\mathrm{OLEDs}$

We report four bipolar molecules, namely m-CzPym, p-CzPym, m-CzTrz, and p-CzTrz, with carbazole (Cz) donor and a benzonitrile-substituted pyrimidine (Pym) or triazine (Trz) acceptor core were synthesized and characterized. The electron deficiency of heteroaryl cores together with the substitution pattern of benzonitrile were employed to tune the energy levels as well as the thermally activated delayed fluorescence (TADF) characteristics. The four molecules exhibited TADF behavior with inferior photoluminescent quantum yields (PLQYs) that limit their applications as emitters. These bipolar molecules were employed as TADF host materials for the benchmark TADF emitter 4CzIPN to achieve high-performing green TADF organic light-emitting diodes (OLEDs). Among the molecules, m-CzPym-hosted TADF-OLEDs achieved a maximum external quantum efficiency (EQE max ) of 31.5%, maximum power efficiency (PE max ) of 95.6 lm/W, and maximum current efficiency (CE max ) of 100.2 cd/A. Notably, p-CzPym-hosted TADF-OLEDs also achieved a PE max of 116.5 lm/W, turn-on voltage of 2.5 V, and impressive low efficiency roll-off performance (>89% of EQE max at 5000 cd/m 2 ), representing one of the highest efficiencies ever reported in 4CzIPN-doped devices. The high device efficiency can be ascribed to the balanced ambipolar carrier-transporting character of the host materials and high PLQY as well as the outstanding light outcoupling efficiency of the emitting layer.

42 ENGINEERING↗

Advanced Cost-Effective Coal-Fired Rotating Detonation Combustor for High Efficiency Power Generation

Coal-based detonation as a means of heat addition in power generation devices is a revolutionary “out of the box” technology concept, wherein it is theoretically possible to achieve an increase in total pressure across the combustor as opposed to a loss of available energy as is the case in conventional combustion systems. Detonation is a revolutionary technology concept of pressure gain combustion that exploits pressure rise from the combustion process to augment high flow momentum and pressures. Recognizing that pressure gain combustion has become a topic of elevated national interest, high energetic modes (exergy) pressure gain combustion (PGC) systems are required. Pressure gain combustion is a key enabling technology for maintaining technological superiority in the development of advanced power generation systems. Pressure gain combustion is an innovative scheme of turbulent combustion that considerably increases thermodynamic cycle efficiencies (~10-20%). This project extends the State-of-the-Art by providing the first direct experimental and computational data of operability dynamics and performance characterization data and create the first validated Coal-Fired RDC investigations. Coal-based RDC is an innovative revolutionary technology that would result in high efficiency power generation and reduced of harmful emissions. Also, the fundamental and open source nature of the project will benefit a broader scientific and industry audience who will be able to utilize the outcomes of this project for advancing the coal-fired RDC power generation development.

01 COAL, LIGNITE, AND PEAT↗

Pumped Thermal Energy Storage Using Low-Cost Particles and a Fluid Bed Heat Exchanger for Maximum Power Efficiency

Robust, efficient, cost-effective long-duration electricity storage (LDES) solutions can enhance grid resiliency, support existing transmission and distribution infrastructure, and enable a greater share of low-cost, variable alternative energy sources to penetrate the market. To meet this need, the project team at the National Renewable Energy Laboratory developed a transformative LDES system based on pumped thermal energy storage (TES) using low-cost particles and a fluid bed heat exchanger for maximum power efficiency (PUMP). The PUMP system is composed of high-temperature, low-cost particle TES coupled with an advanced pressurized fluid bed heat exchanger (PFB HX) that supports a high-efficiency pumped thermal energy storage (PTES) system integrated with concentrating solar thermal power (CSP). The PUMP project developed and de-risked a PFB HX and particle CSP system intended to be integrated with reversible turbomachinery and a modeling tool to assess PTES cost and performance.

14 SOLAR ENERGY↗

Training self-learning circuits for power-efficient solutions

As the size and ubiquity of artificial intelligence and computational machine learning models grow, the energy required to train and use them is rapidly becoming economically and environmentally unsustainable. Recent laboratory prototypes of self-learning electronic circuits, such as “physical learning machines,” open the door to analog hardware that directly employs physics to learn desired functions from examples at a low energy cost. In this work, we show that this hardware platform allows for an even further reduction in energy consumption by using good initial conditions and a new learning algorithm. Using analytical calculations, simulations, and experiments, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution—greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving the power efficiency given a specific tolerance to error.

Stern, Menachem (ORCID:0000000302158082)↗

Efficiency, Power Loss, and Power Factor Measurement of Quadrupole Magnet Power Supplies at the Spallation Neutron Source

The linear accelerator (LINAC) quadrupole magnets are powered by 42 silicon-controlled rectifier (SCR) based power supplies at the Spallation Neutron Source (SNS) facility of Oak Ridge National Laboratory. These 35V, 525A power supplies are bulky, inefficient and require both air and water cooling. The reliability of the SNS facility is impacted due to water leaks internal to power supplies and current readback issues associated with their unique control system interface, resulting in multiple downtime events. Hence, an alternate solution is necessary for the continued reliable operation of the SNS. To mitigate the above-mentioned problems, this paper proposes the use of off-the-shelf Switch Mode Power Supplies (SMPS) rated for 20V, 500A with serial control interface. These SMPS are air-cooled, more efficient and more compact owing to their switching speeds of approximately 160 kHz. The performance enhancements of the SMPS in comparison with the existing SCR power supply are discussed in detail in this paper. The features of the SMPS, along with experimental results for both power supplies, like efficiency, power losses and stability, are presented. Ongoing work is also discussed.

Harave, Sudarshan↗

An Efficient Power Flexibility Aggregation Framework via Coordinate Transformation and Chebyshev Centering Optimization

The large-scale integration of distributed energy resources (DERs) converts the role of a distribution system from a customer to a prosumer. In this context, the controllable DERs are expected to provide capacity support for the transmission system, which can be considered as power flexibility aggregation. Specifically, it is a process of controlling the power output of DERs to fulfill the desired capacity or flexibility. However, the power output of DERs as control variables is redundant. That is, a power flexibility command can be realized by multiple dispatch alternatives, which may hinder efficient design of control rules and flexibility evaluation schemes. Therefore, this paper introduces a conception of the artificial power flexibility controller to avoid the redundancy issue, which is a converted control variable after reconstructing the DER power region. Through this reconstruction, the DERs can be indirectly controlled by the proposed artificial power flexibility controller. In addition, a Chebyshev centering optimization model is developed to approximate the power flexibility region. At last, the proposed method is verified on an artificially-designed network with multiple DERs.

Chebyshev centering optimization↗

SPUTTERED THIN FILMS FOR VERY HIGH POWER, EFFICIENT, AND LOW-COST COMMERCIAL SOFCS

The aim of this project was to leverage the low area specific resistance (ASR) of Redox’s GDC electrolyte-based cell architecture and increase solid oxide fuel cell (SOFC) efficiency (i.e., open circuit voltage, or OCV) without significantly increasing cell resistance. The key to achieving the increased SOFC efficiency is the introduction of a thin sputtered yttria stabilized zirconia (YSZ) electron-blocking layer and a thin sputtered gadolinia doped ceria (GDC) barrier layer on top of the half-cell substrate (i.e., anode and GDC electrolyte). A key initial effort of the project was to improve the quality of the half-cell substrate surface so that any remaining defects were significantly smaller than the desired film thickness. During lab-scale trials, we had to overcome challenges with film cracking during post-sputtering treatments (e.g., thermal anneals) as well as damage to the sputtering targets. After the initial lab-scale trials, the focus shifted toward the use of commercial scale sputtering equipment with a sputtering equipment manufacturer in the microelectronics industry. Using the commercial sputtering equipment, we deposited films with different variations of power, PO2, sputtering time, platen speed, etc. We then determined the combinations of sputtering conditions and post-sputtering treatments that yielded high-quality films without cracks or other significant defects. While thin films were deposited on cells as large as 10 cm by 10 cm, the processing was optimized using 4 cm by 4 cm cells. Cell performance was evaluated in stainless-steel test fixtures between 500 °C and 700 °C with hydrogen fuel fed to the anode and air fed to the cathode. Extensive studies allowed us to determine that modified cathode and cathode contact firing processes were required to achieve theoretical OCV. Moreover, use of the new firing processes resulted in the need for a modified cathode contact to achieve a low ASR. In summary, the project demonstrated the performance of high OCV (1.13 V at 650 °C) from sputtered layers and a low ASR (~0.25 Ohms-cm2 at 650 °C) resulting from a modification of cathode/contact processing and the introduction of alternative contact layers that are sufficient to yield a Gen-1 cell with a maximum power density of approximately 1.2 W/cm2. At an operating voltage of 0.74 V, this would yield a cell power density of ~1.1 W/cm2. While not utilized in this project, a Redox Gen-2 cell has a catalyst-infiltrated porous anode that reduces the ASR by more than 50% from that of the Gen-1 cells used in this project. Therefore, if the sputtered YSZ electron-blocking layer and GDC barrier layer are added to a Gen-2 half cell with a similar increase in OCV to the theoretical value of ~1.13 V at 650 °C, and if the same improvement in ASR (from that demonstrated in this project) is achieved when using the Gen-2 half-cell architecture as a sputtered cell substrate, then the power density at 0.74 Vop could be as high as ~2.6 W/cm2. The impact of such power density gains, while still maintaining high cell efficiency, and thus high system efficiency, is a dramatic decrease in system cost because the stack represents ~30-40% of the SOFC system cost.

01 COAL, LIGNITE, AND PEAT↗

Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Abstract As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.

Optics↗

Optics Enabled Networks and Architectures for Data Center Cost and Power Efficiency

Bandwidth demand for datacenter networks continues as performance increases and is further fueled by the exploding demand for AI and new HPC workloads. Managing power and costs will require a range of solutions including new networking and workload specialized architectures, composable systems and optical circuit switching. In this study we focus primarily on two topics, examining the benefits of flatter networks (enabled mainly by means of co-packaged-optics-enabled switches) and the utilization improvement potential for composable (disaggregated) systems, while discussing specialized hardware and networks, and optical circuit switching more briefly.

99 GENERAL AND MISCELLANEOUS↗

Ultra-power-efficient heterogeneous III–V/Si MOSCAP (de-)interleavers for DWDM optical links

We discuss the design and demonstration of various III–V/Si asymmetric Mach–Zehnder interferometer (AMZI) and ring-assisted AMZI (de-)interleavers operating at O-band wavelengths with 65 GHz channel spacing. The wafer-bonded III–V/Si metal-oxide-semiconductor capacitor (MOSCAP) structure facilitates ultra-low-power phase tuning on a heterogeneous platform that allows for complete monolithic transceiver photonic integration. The second- and third-order MOSCAP AMZI (de-)interleavers exhibit cross-talk (XT) levels down to − 22 dB and − 32 dB with tuning powers of 83.0 nW and 53.0 nW, respectively. The one-, two-, and three-ring-assisted MOSCAP AMZI (de-)interleavers have XT levels down to − 27 dB, − 22 dB, and − 20 dB for tuning powers of 10.0 nW, 7220.0 nW, and 33.6 nW, respectively. The leakage current density is measured to be in the range of 1.6 – 27 μA / cm 2 . To the best of our knowledge, we have demonstrated for the first time, athermal III–V/Si MOSCAP (de-)interleavers with the lowest XT and reconfiguration power consumption on a silicon platform.

Cheung, Stanley (ORCID:0000000248860013)↗

Unveiling the influence of selective-area-regrowth interfaces on local electronic properties of GaN p-n junctions for efficient power devices

Here, we report correlated nanoscale mapping of the structure, composition, and properties of regrown GaN p-n junctions to identify how etching and non-planar regrowth processes limit diode performance via the introduction of unintentional dopants and defect states. p-GaN was selectively regrown in n-GaN trenches with SiO 2 masks of variable mask-to-trench-width ratio. Dilute Al layers were periodically introduced during regrowth as markers of the growth interface. Correlated nanoscale mapping of doping, conductivity, and dopant complexes was achieved with atom probe tomography (APT), scanning spreading resistance microscopy (SSRM), and cathodoluminescence (CL) spectroscopy, respectively. The Al marker layers, detected by APT, enabled reconstruction of the faceted growth interface and correlation of the dopant concentration with position and time. The p-GaN growth rate is proportional to the mask-to-trench width ratio while the dopant incorporation rate is invariant. At trench edges, magnesium incorporation is suppressed, and oxygen incorporation enhanced, due to preferential incorporation on the semi-polar growth surface, leading to compensation and less abrupt p-n junctions; the SiO 2 mask is a source of oxygen. Residual etch damage below the regrowth interface induces n-type and p-type conductivity, creating leakage pathways. The non-uniform Mg incorporation is driven by crystal anisotropy and is thus inherent to non-planar regrowth, but can be mitigated by engineering the regrowth interface and process parameters. The unprecedented integration of spatially resolved mapping of dopants, impurities, conductivity, and carrier type is a powerful approach to discriminating distinct factors that limit the performance of regrown diodes, enabling the rational optimization of process and device design.

36 MATERIALS SCIENCE↗

Economic Long-Duration Electricity Storage by Using Low-Cost Thermal Energy Storage and High-Efficiency Power Cycle (ENDURING)

The project involved considerable industry participation including General Electric (GE, power cycle analysis), Babcock & Wilcox (B&W), Allied Mineral Products (insulation materials), and Worley-Advisian (system integration). They provided insights on their products or services for component and system designs. The project successfully achieved the project goals of demonstrating technology feasibility for long duration energy storage with a technology path towards 5c/kWh levelized cost of storage. The project has generated four patent applications (one awarded patent and three under review), three published journal papers, a draft book chapter, and five manuscripts under preparation, with several conference papers and numerous presentations including invited talks. An intellectual properties (IP) option agreement has been signed with an U.S. manufacturer, with interests from energy developers. Four related projects with $5.5 million DOE funding support more developments for long duration energy storage and broad industry decarbonization.

25 ENERGY STORAGE↗

Devices and methods for increasing the speed or power efficiency of a computer when performing machine learning using spiking neural networks

A method for increasing a speed and efficiency of a computer when performing machine learning using spiking neural networks. The method includes computer-implemented operations; that is, operations that are solely executed on a computer. The method includes receiving, in a spiking neural network, a plurality of input values upon which a machine learning algorithm is based. The method also includes correlating, for each input value, a corresponding response speed of a corresponding neuron to a corresponding equivalence relationship between the input value to a corresponding latency of the corresponding neuron. Neurons that trigger faster than other neurons represent close relationships between input values and neuron latencies. Latencies of the neurons represent data points used in performing the machine learning. A plurality of equivalence relationships are formed as a result of correlating. The method includes performing the machine learning using the plurality of equivalence relationships.

Vineyard, Craig Michael↗