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At least 73 records · Page 4

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DOE OSTI2026

Elastic-wave sensitivity-guided adaptive seismic survey design for cost-effective monitoring of geological carbon storage

Effective seismic monitoring is essential for verifying CO₂ containment, detecting potential leakage, and optimizing operational decisions in geologic carbon storage. Here, this study presents a time-adaptive, elastic-wave sensitivity-guided framework for designing cost-effective seismic monitoring layouts for tracking CO₂ plume migration. The method is based on elastic-wave sensitivity analysis, which quantifies how variations in subsurface properties impact seismic wavefields. Two complementary design strategies are developed: one based on selecting a fixed number of seismic sources (Method A), and the other based on selecting source–receiver pairs contributing to a fixed fraction of cumulative elastic-wave sensitivity energy (Method B). The optimization workflow to identify source–receiver configurations with the highest detection potential is demonstrated using a hypothetical GCS scenario at the Kimberlina site in California using simulations of elastic-wave sensitivity data at multiple post-injection timesteps. Results show that both strategies adapt to evolving plume geometries and wavefield sensitivities, with Method B offering broader spatial coverage and Method A ensuring simpler deployment. This framework enables site-specific, cost-effective, and risk-informed seismic survey designs, enhancing the ability to monitor CO₂ migration over time in evolving geological environments

58 GEOSCIENCES↗

DOE OSTI2021

Topology optimization of 3D photonic crystals with complete bandgaps

The design of photonic crystals with complete bandgaps has recently received considerable research focus for numerous reasons. This work leverages well-known nonlinear programming techniques to alleviate the non-smoothness caused by degenerate eigenvalues such that topology optimization problems can be solved with the open-source IPOPT software. A fully-vectorial plane wave expansion technique is used with an iterative eigensolver to efficiently predict dispersion properties of candidate structures. Nonlinear programming is employed to solve the inverse problem of designing three-dimensional periodic structures that exhibit complete two-dimensional (2D) and three-dimensional (3D) photonic bandgaps. Mesh refinement is performed to alleviate the large computational burden of designing and analyzing photonic crystals, and a periodic density filter is implemented to impose a minimum feature size for manufacturability considerations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Single-qubit multi-party transmission using universal symmetric quantum cloning

This study considers the hypothetical quantum network case where Alice wishes to transmit one qubit of information (specifically a pure quantum state) to M parties, where M is some large number. The remote receivers locally perform single-qubit quantum state tomography on the transmitted qubits in order to compute the quantum state within some error rate (dependent on the tomography technique and the number of transmitted qubits). We show that with the use of an intermediate optimal symmetric universal quantum cloning machine (between Alice and the remote receivers) as a repeater-type node in a hypothetical quantum network, Alice can send significantly fewer qubits compared to direct transmission of the message qubits to each of the M remote receivers. This is possible due to two properties of quantum cloning. The first is that single qubit quantum clones retain the same Bloch angle as the initial quantum state. This means that if the mixed state of the quantum clone can be computed to high enough accuracy, the original pure quantum state can be inferred by extrapolating that vector to the surface of the Bloch sphere. The second property is that the state overlap of approximate quantum clones, with respect to the original pure quantum state, quickly converges (specifically for 1 → M , the limit of the fidelity as M goes to infinity is $\frac{2}{3}$). This means that Alice can prepare a constant number of qubits (which are then passed through the quantum cloning machine) in order to achieve a desired error rate if M is large enough. Combined, these two properties mean that for a large M , Alice can prepare many orders of magnitude fewer qubits in order to achieve the same single qubit transmission accuracy compared to the naive direct qubit transmission approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Multi-User Capacity for Cyclic Prefix Direct Sequence Spread Spectrum with Linear Detection and Precoding

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a promising solution for futuristic 6G ultra-reliable low latency communications (URLLC) and massive machine type communication (mMTC) applications, where the CP-DSSS waveform would operate as a secondary network at the same frequencies as the primary network but at much lower SNR. In this paper, we show per-user capacity for multi-user scenarios, where simple matched filtering (MF) is performed on the uplink (UL) and time-reversal (TR) precoding is used on the downlink (DL). When operating in the low SNR regime, CP-DSSS achieves per-user capacity near the optimum single-user capacity by using a MF detector at the receiver for the UL. TR precoding converges to the optimal capacity as the number of antennas at the hub/gateway increases. Given the near-optimal performance of MF detection and TR precoding for each of the users, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed 6G networks.

5G and Beyond Communications↗

DOE OSTI2024

Entanglement-Enhanced Neyman-Pearson Target Detection

Quantum illumination (QI) provides entanglement-enabled target-detection enhancement, despite operating in an entanglement-breaking environment. Existing experimental studies of QI have utilized a Bayesian approach, assuming that the target is equally likely to be present or absent before detection, to demonstrate an advantage over classical target detection. However, such a premise breaks down in practical operational scenarios in which the prior probability is unknown, thereby hindering QI's applicability to real-world target-detection scenarios. In this work, we adopt the Neyman-Pearson criterion in lieu of the error probability for equally likely target absence or presence as our figure of merit for QI. We demonstrate an unconditional quantum advantage over the optimal classical-illumination protocol as benchmarked by the receiver operating characteristic, which examines detection probability versus false-alarm probability without resorting to known prior probabilities. Our work represents a critical advancement in adapting quantum-enhanced sensing to practical operational settings.

Ward, William↗

DOE OSTI2020

Optimizing branch re-wiring in a software instruction cache

A method includes a computer device receiving a branch instruction; the computer device managing two tables, where a first table relates to application blocks and a second table relates to available address slots; and the computer device calculating a target of the branch instruction using a branch-to-link register, the computer device optimizes re-wiring in a cache using the calculation and the managed two tables.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Real time feedback-based optimization of distributed energy resources

An example device includes a processor configured to receive a plurality of voltage values corresponding to voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Real time feedback-based optimization of distributed energy resources

An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Dall'Anese, Emiliano↗

DOE OSTI2023

Real time feedback-based optimization of distributed energy resources

Dall'Anese, Emiliano↗

DOE OSTI2024

Autonomous online optimization of a closed-circuit reverse osmosis system

As freshwater becomes increasingly scarce, many industrial and municipal water utilities look at premise-scale water treatment and reuse to meet water demand. Closed-circuit reverse osmosis (CCRO) has been proposed as a promising process design to do so. This sequencing batch process enables operation at higher brine salinity levels by means of a recycle flow. Optimal operation requires that the maximum salinity level at the membrane surface represents an optimal trade-off between brine disposal costs and energy efficiency. This maximum salinity level may change over time as the feed water composition changes and electricity markets fluctuate. In this article, we present the results of the experimental evaluation of an automatic technique for continuous online optimization, known as extremum seeking control. This technique has a long history in the process control community but has received little traction so far in the water industry. We modify this technique to enable its use for online optimization of CCRO, specifically to account for its sequential batch operation. We challenge the optimization schemes through several experimental tests and illustrate the advantages and drawbacks of extremum-seeking control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Water limiting current measurements in anion exchange membrane fuel cells (AEMFCs); part 1: Water limiting current method development

We report water management is critical in optimizing anion exchange membrane fuel cell (AEMFCs) performance and durability. The role of membranes on determining cell water balance in AEMFCs has received limited study. A more thorough understanding of membrane water transport parameters is necessary for thorough cell optimization. In this study, a novel method for probing limiting current based on water flux across an AEMFC is presented for the first time. The water limiting current has been investigated as a function of relative humidity of anode and cathode streams, cathode backpressure and cell operating temperature. From these studies, it is clearly shown that water diffusion through the membrane from the anode is the most critical mechanism for achieving high current density performance and durability. The developed method can act as a novel screening technique that focuses on the impacts of water transport parameters and helps identify promising membrane materials based on their water transport properties. A critical but underappreciated facet of AEMFC performance and durability.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified a principal set of 20 time-invariant markers, including 16 single nucleotide polymorphisms and two HLA-DR genotypes, gestational age, and exposure to a prebiotic formula. Integration of the metabolome identified 22 high-priority metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies, dependent upon the time horizon. The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to 3 pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate pathway. TEDDY data suggest that these metabolic processes may play a role in triggering islet autoimmunity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Overview of the TCV tokamak program: Scientific progress and facility upgrades

The TCV tokamak is augmenting its unique historical capabilities (strong shaping, strong electron heating) with ion heating, additional electron heating compatible with high densities, and variable divertor geometry, in a multifaceted upgrade program designed to broaden its operational range without sacrificing its fundamental flexibility. The TCV program is rooted in a three-pronged approach aimed at ITER support, explorations towards DEMO, and fundamental research. A 1 MW, tangential neutral beam injector (NBI) was recently installed and promptly extended the TCV parameter range, with record ion temperatures and toroidal rotation velocities and measurable neutral-beam current drive. ITER-relevant scenario development has received particular attention, with strategies aimed at maximizing performance through optimized discharge trajectories to avoid MHD instabilities, such as peeling-ballooning and neoclassical tearing modes. Experiments on exhaust physics have focused particularly on detachment, a necessary step to a DEMO reactor, in a comprehensive set of conventional and advanced divertor concepts. The specific theoretical prediction of an enhanced radiation region between the two X-points in the low-field-side snowflake-minus configuration was experimentally confirmed. Fundamental investigations of the power decay length in the scrape-off layer (SOL) are progressing rapidly, again in widely varying configurations and in both D and He plasmas; in particular, the double decay length in L-mode limited plasmas was found to be replaced by a single length at high SOL resistivity. Experiments on disruption mitigation by massive gas injection and electron-cyclotron resonance heating (ECRH) have begun in earnest, in parallel with studies of runaway electron generation and control, in both stable and disruptive conditions; a quiescent runaway beam carrying the entire electrical current appears to develop in some cases. Developments in plasma control have benefited from progress in individual controller design and have evolved steadily towards controller integration, mostly within an environment supervised by a tokamak profile control simulator. TCV has demonstrated effective wall conditioning with ECRH in He in support of the preparations for JT-60SA operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Intelligent driving passive pedal control

A method for assignment of vehicle control includes receiving route data indicating a route between a starting location of a vehicle and a destination location, and determining an optimal vehicle configuration for the route based on a target vehicle speed and a hybrid torque split. The method further includes receiving a driver requested torque value and determining a passive pedal torque value based on the route data and vehicle powertrain data. The method further includes selectively assigning control of the vehicle to a vehicle system or to a driver of the vehicle based on the driver requested torque value and the passive pedal torque value.

Aggoune, Karim↗

DOE OSTI2020

In-Depth Optimization with the OpenACC-to-FPGA Framework on an Arria 10 FPGA

The reconfigurable computing paradigm that uses field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs' unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. The relationships between FPGA resource usages and runtime performance were also explored.

Lambert, Jacob↗

DOE OSTI2022

Planar Common-Mode EMI Filter Design and Optimization for High-Altitude 100-kW SiC Inverter/Rectifier System

Not provided.

Engineering↗

DOE OSTI2021

Optimizing Vehicle Fleet and Assignment for Concentrating Solar Power Plant Heliostat Washing

Concentrating solar power central-receiver plants use thousands of sun-tracking mirrors, i.e., heliostats, to reflect sunlight to a central receiver, which collects and uses the heat to generate electricity. Over time, soiling reduces the reflectivity of the heliostats and, therefore, the efficiency of the system. Current industry practice sends vehicles to wash heliostats in an ad hoc fashion. We present a mixed-integer nonlinear program that determines wash vehicle fleet size, mix, and assignment of wash crews to heliostats to minimize the sum of (i) the revenues lost due to heliostat soiling, (ii) the costs of hiring wash crews and operating the vehicles, and (iii) the costs of purchasing wash vehicles. We establish conditions for convexity of the objective function, and then propose a decomposition method that enables near-optimal solutions to the wash vehicle fleet sizing and assignment problem on the order of a couple of minutes. Furthermore, these solutions yield hundreds of thousands of dollars in savings per year over current industry practices.

14 SOLAR ENERGY↗