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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Printing polymer blends through in situ active mixing during fused filament fabrication

Fused filament fabrication (FFF) enables production of 3D objects over a range of material compositions at low-cost relative to traditional manufacturing approaches. To date, a limited but growing number of materials are able to be used with FFF, however many applications exist where specific mechanical, thermal, or chemical properties are needed that cannot currently be met with the available feedstock selection. Therefore, a need exists to tune these materials for specific chemical or mechanical properties. One common formulation strategy to address these demanding design parameters is to develop composites or polymer blend filaments. Typically, this is a time-consuming and costly optimization process. Here, we have developed hardware for reproducibly mixing two filaments of similar or dissimilar compositions at the time of printing within individual printed layers. This mixing occurs via software-controlled rotating hardware in the chamber of an extruder’s hot-end. The efficiency of mixing within the printed layers has been characterized in detail as a function of the rotational speed and geometry of the blending hardware. These parameters were exploited to program the ratio and distribution of thermoplastic-based filaments blended within printed extrudate. Example printed specimens were produced with thermoplastic polyurethane (TPU) elastomer blended with rigid polylactic acid (PLA) and Nylon blended with PLA. In addition, a conductive carbon nanotube (CNT)-PLA composite was blended as a function of mixer geometry and input feed ratios with non-conductive PLA and resistance values were measured across the resulting printed specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The ePIC dual-radiator RICH detector

The dual radiator Ring Imaging Cherenkov (dRICH) detector is required to provide continuous hadron identification from ≈3 GeV/c up to ≈50 GeV/c, and to supplement electron and positron identification from a few hundred MeV/c up to about 15 GeV/c, in the forward (ion-side) end-cap of the ePIC experiment. Such an extended momentum range imposes the use of two radiators, gas and aerogel. The common imaging system, that ensures compactness and cost-effectiveness, is based on SiPM sensors to work in a high non-uniform magnetic field. During the R&D phase, the dual radiator principle and the single component performance have been validated. A status overview of the project is presented. The design and technological choices are discussed together with the results obtained from laboratory characterization of the component demonstrators and beam tests of the evolving prototypes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

The load profiles of most commercial and industrial consumers are characterized by brief periods of very high power consumption followed by intervals of lower demand. To encourage such consumers to flatten their load profiles, power utilities in and around the world often levy a monthly demand charge (DC) on the peak demand measured over brief intervals. In this work, we consider the joint optimization of energy costs (EC) and the instantaneous peak power of a multi-unit building which uses a hydronic heating, ventilation and cooling (HVAC) system and responds to a demand response (DR) program. Despite the non-linear structure of the problem, we show how optimal solutions can be obtained efficiently using linear programming. Next, we study the power demand patterns resulting from our proposed strategy for thermostatically controlled loads (TCLs), and evaluate the strategy’s performance for various climate zones in the US, under both typical and atypical weather conditions. Finally, the results show that depending on the ambient conditions and the tariff structure, our strategy can result in utility bill savings of up to nearly 19% compared to the baseline. The results also indicate that our power control strategy can significantly reduce the instantaneous peak power consumption in commercial TCLs.

HVAC↗
DOE OSTI2022

Towards durable resistance to Septoria stem canker and leaf spot: A molecular understanding of resistance (Final Technical Report)

To identify, validate, and functionally characterize alleles that confer resistance to Septoria canker and leaf spot in Populus. The proposed research will elucidate a major mechanism of resistance to Sphaerulina musiva, the major limiting factor to plantations in eastern North America. Genome-wide association mapping, CRISPR/Cas9, and protein-protein assays will be used, enabling marker-aided breeding, reducing costs, and accelerating development of resistant varieties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

DISCOVARY PROJECT: Material Background Report

UC/UO 2 composites have been proposed as a next generation fuel for light water reactors (LWRs). Accident tolerant fuels (ATF) have been a focus in the Advanced Fuels Campaign (AFC) to improve the safety and performance of LWRs and includes research and development efforts on the cladding and fuel. The 10 wt.% UC/UO 2 composite fuel was selected as a result of an extensive literature review and was selected due to the improvement of the fuel cycle cost. The inclusion of a UC phase in the composite material improves on the properties of standard UO 2 by increasing the uranium density of the fuel and increasing the thermal conductivity. Significant development has been carried out to refine the processing and sintering parameters and has led to a dense composite without ternary phases present. Characterization of the chemistry and microstructure has been carried out to send ahead of neutron irradiations in the Belgium Research Reactor (BR2) at SCK-CEN.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Dynamic Electric Field Assisted CO 2 Methanation

Energy-efficient advanced chemical reactions are essential for accelerating the growth of hydrocarbon economy. Sabatier reaction stands out for its potential to effectively transform carbon dioxide into valuable hydrocarbons and is an asset for long-duration Mars missions. This study explores a novel catalytic approach that harnesses electric field-assisted catalysis to substantially enhance the efficiency of the Sabatier reaction. Application of a dynamically perturbed electric field at 1000 Hz resulted in remarkable enhancements, increasing methane formation rates by over 100% at 350 °C and by 74% at 400 °C. Post-reaction catalyst characterization further revealed reduced blockage of active catalytic surface area under the applied electric field, emphasizing improved catalytic longevity and sustained activity. These results underscore the potential of tailored electric field waveforms to dynamically modulate elementary reaction kinetics and surface processes, positioning electric field-assisted catalysis as a transformative strategy for energy-efficient, cost-effective chemical manufacturing and energy conversion technologies.

42 - ENGINEERING↗
DOE OSTI2024

Novel alkali intercalated and acid-exfoliated biochars with enhanced surface areas for contaminant adsorption applications

Cost-effective and eco-friendly adsorbents are essential in environmental engineering and biochar is a promising material from the perspective. A novel and efficient surface modification approach involving alkali intercalation and acid exfoliation was designed in this study to enhance the physicochemical properties of biochar. Here, the alkali intercalation process utilizes potassium hydroxide (KOH), while acid exfoliation involves varying HNO 3 , H 2 SO 4 , and H 3 PO 4 concentrations. A simple two-stage pyrolysis process was employed to facilitate the intercalation-exfoliation modification. The modified biochars were characterized using BET, SEM, XRD, etc., to understand physicochemical properties. To quantify the effectiveness of the modifications, adsorption of malachite green dye as a model moiety was investigated. Dye removal sorption rates exceeding 99 % were recorded in the case of the biochars modified through a two-step process using KOH and 0.1MH 3 PO 4 . Specifically, the highest contaminant removal of 99.9 % was recorded when 60 mg of the KOH-0.1MH 3 PO 4 biochar was employed, which is significantly higher than unmodified biochar’s 45.41 % removal at a higher dosage of 100 mg. Moreover, the adsorption kinetics revealed that all the modified biochars attained the maximum removal concentrations (~99 % removal) in a mere 300 min, indicating a tenfold improvement in adsorption rate from unmodified biochar's requirement of over 5000 min. The results achieved through this study provide a cost-effective, fast, and environment-friendly technology for enhancing the adsorption characteristics and performance of biochars toward contaminant removal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking High Capacity and Reversible Alkaline Iron Redox Using Silicate-Sodium Hydroxide Hybrid Electrolytes

Alkaline iron (Fe) batteries are attractive due to the high abundance, low cost, and multiple valent states of Fe but show limited columbic efficiency and storage capacity when forming electrochemically inert Fe 3 O 4 on discharging and parasitic H 2 on charging. Herein, sodium silicate is found to promote Fe(OH) 2 /FeOOH against Fe(OH) 2 /Fe 3 O 4 conversions. Electrochemical experiments, operando X-ray characterization, and atomistic simulations reveal that improved Fe(OH) 2 /FeOOH conversion originates from (i) strong interaction between sodium silicate and iron oxide and (ii) silicate-induced strengthening of hydrogen-bond networks in electrolytes that inhibits water transport. Furthermore, the silicate additive suppresses hydrogen evolution by impairing energetics of water dissociation and hydroxyl de-sorption on iron surfaces. In conclusion, this new silicate-assisted redox chemistry mitigates H 2 and Fe 3 O 4 formation, improving storage capacity (199 mAh g -1 in half-cells) and coulombic efficiency (94 % after 400 full-cell cycles), paving a path to realizing green battery systems built from earth-abundant materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

14 SOLAR ENERGY↗
DOE OSTI2020

A Digital Imaging Method for Evaluating the Kinetics of Vapochromic Response

This work describes the use of a cell phone camera and the L*a*b method (color space specified by the International Commission on Illumination) to characterize the color change in different vapochromic systems. In this study we have developed a semi-automatic color change analysis software that digitally analyzes images (e.g., video frames) collected while a vapochromic material is absorbing vapor. The advantages of using this method, as compared to reflectance spectroscopy or transmission spectroscopy through a thin film, include low cost, convenience, portability, ease of sample preparation, the absence of need for specialized equipment, and the ease of simultaneously collecting data on different samples under identical conditions. In addition, this method arguably provides direct insight into what a human would observe when monitoring these color changes by eye. Limitations of the method also are discussed in this paper.

cell phone camera, colorimetric sensing, vapor det↗
DOE OSTI2023

Mapping the pathways of photo-induced ion migration in organic-inorganic hybrid halide perovskites

Abstract Organic-inorganic hybrid perovskites exhibiting exceptional photovoltaic and optoelectronic properties are of fundamental and practical interest, owing to their tunability and low manufacturing cost. For practical applications, however, challenges such as material instability and the photocurrent hysteresis occurring in perovskite solar cells under light exposure need to be understood and addressed. While extensive investigations have suggested that ion migration is a plausible origin of these detrimental effects, detailed understanding of the ion migration pathways remains elusive. Here, we report the characterization of photo-induced ion migration in perovskites using in situ laser illumination inside a scanning electron microscope, coupled with secondary electron imaging, energy-dispersive X-ray spectroscopy and cathodoluminescence with varying primary electron energies. Using methylammonium lead iodide and formamidinium lead iodide as model systems, we observed photo-induced long-range migration of halide ions over hundreds of micrometers and elucidated the transport pathways of various ions both near the surface and inside the bulk of the samples, including a surprising finding of the vertical migration of lead ions. Our study provides insights into ion migration processes in perovskites that can aid perovskite material design and processing in future applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Engineered enzymatic cascade converts diols to amino alcohols

Aliphatic amino alcohols such as 6-amino-1-hexanol are potential platform chemicals for a variety of advanced materials, but applications are currently limited by reagent costs. Aliphatic amino alcohols can currently be synthesized from biomass-derived diols at elevated temperatures and pressures using Ru-based catalysts that produce a mixture of amino-alcohol, diamine, and cyclic amine products. Replacing chemical amination with an enzymatic cascade would reduce resource needs and enable reactions under milder conditions. In this work, we characterized a two-enzyme cascade that selectively converts C4–C7 diols to the corresponding amino alcohols under aqueous conditions at room temperature and pressure. By engineering the rate-limiting enzyme and optimizing reaction conditions, we increased amino alcohol production nearly 30-fold, achieving a selectivity of 99%. The same enzyme cascade could also be used to convert amino alcohols into cyclic amines through reduction of the corresponding cyclic imine. This engineered cascade provides a green opportunity to sustainably synthesize asymmetric bifunctional platform chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Demonstration and Validation of Additively Manufactured Intensified Device for Enhanced Carbon Capture

In FY 2018 and 2019, ORNL personnel designed, prototyped, characterized, and tested the enhancement of amine-based carbon capture using additively manufactured intensified devices, i.e., devices combining multiple thermodynamic and kinetic processes into one unit operation. State of the art scrubbing technology often employs inter-stage cooling which requires multiple stages of single-purpose unit operations, leading to larger equipment size, higher capital cost, and sometimes less than optimal operating conditions for each piece of equipment. It was proposed that integrating multiple thermodynamic and kinetic operations into one unit could mitigate or negate these drawbacks. Heat and mass transfer studies demonstrated that an additively manufactured intensified device could significantly improve capture efficiency at bench scale. This project is a continuation of FEAA130 and aims at (1) demonstration and validation of enhanced CO 2 capture with 3D printed intensified devices (i.e., mass exchanger packing with internal cooling channels), for low-aqueous-solvent (LAS) based capture at the ORNL-constructed laboratory-scale (4.5-feet tall) column, namely Column A, with built-in cooling and (2) computational scoping on the conceptual design of a flexible and modular larger column, namely Column B, at ORNL for further demonstration of enhanced capture for aqueous solvent. To execute this multi-faceted project scope, an integrated project team from ORNL’s Manufacturing Science Division is tasked with applying capabilities in computational fluid dynamics (CFD), additive manufacturing, and absorber-scale demonstration/validation experiments.

42 ENGINEERING↗
DOE OSTI2020

Laboratory Validation of Integrated Lighting Systems Retrofit Performance and Energy Savings

Light-emitting diodes (LED) fixtures and lamps have emerged as leading technologies for general illumination and are a well-established energy efficiency retrofit measure in commercial buildings (from around 2% of installed fixtures and lamps in 2013 to 28% by 2020). Retrofit approaches that integrate elements, such as networked controls, daylight dimming, and advanced shade technologies lag in comparison. Integrated retrofits have been shown to increase savings over single end-use retrofits, but are perceived as higher complexity and risk. More validation of integrated lighting system performance is needed. This study presents results from laboratory testing of three packages combining fixtures, networked controls, task tuning, and daylight dimming, advanced shades, and lighting layout changes. We characterize performance in perimeter open-office zones, finding energy savings from 20% for daylight dimming and automated shades (no LED retrofit) to over 70% for LED retrofits with advanced controls and shades or lighting layout changes. We present some implementation details, including lessons learned from installation and commissioning in the laboratory setting. We also discuss cost-benefit analysis approaches for the types of packages presented, including the need to quantify and incorporate energy and non-energy benefits for advanced shades packages, which enhance occupant comfort but add significant cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Nevada Higher Education Benefits from US Department of Energy (DOE) Environmental Management (EM) Nevada Program's Transfer of Geologic Samples - 20442

The Nevada National Security Site (NNSS), formerly the Nevada Test Site, was the location of 100 historic atmospheric and 828 historic underground nuclear tests from 1951 to 1992. Related to this historic nuclear testing, geologic and hydrologic studies of the site were conducted utilizing the skills and the expertise of the U.S. Geological Survey (USGS) and national laboratories (Los Alamos National Laboratory, Lawrence Livermore National Laboratory), who were principal in leading the development of weapons and were responsible for specific underground testing programs. The subsurface samples and technical/scientific data associated with NNSS geologic studies (past and present) are preserved and stored at the USGS Mercury Core Library and Data Center located at the NNSS. Currently, the facility stores over 2,000,000 linear feet of cores and cuttings from more than 2,600 drill holes that can be accessed for study. Most of the samples and historic geologic work was focused directly on subsurface geologic settings that relate directly to historic underground nuclear testing. Since 1992, the United States has observed a unilateral moratorium on full-scale nuclear testing, and the U.S. Department of Energy (DOE) Environmental Management (EM) Nevada Program is now responsible for hydrogeologic characterization of the potential impacts to the natural groundwater systems that may have resulted from historic underground nuclear tests. As part of this effort, the EM Nevada Program Underground Test Area (UGTA) Activity has drilled and completed over 58 deep (2,000 - 7,000 ft.) characterization wells, totaling in excess of 170,000 linear feet of cuttings and core samples. Cutting samples were collected as triplicate samples for each respective depth interval, to account for potential later nondestructive/destructive analysis and to preserve samples for regulatory purposes. Recently, it was recognized by the EM Nevada Program that opportunities may exist to reduce the cost and floor space required for the storage of geologic samples at the Mercury Core Library without impacting the integrity and representative nature of the samples necessary for project execution. An initiative was sponsored by EM Nevada Program to evaluate several options: 1) disposal of a portion of cuttings and cores in a land fill setting; 2) reduction in sample volume through skeletonizing core and cuttings from wells to a representative but much smaller number of samples; and 3) solicit potential interest in the academic community where other geoscientists could freely access the samples for studies. The Nevada state university system through the Nevada Bureau of Mines and Geology responded positively to the opportunity to receive these NNSS samples from the EM Nevada Program. In June 2019, approximately 17,000 geologic samples, representing greater than 170,000 linear feet of drilling and weighing over 20,000 pounds, were shipped from the NNSS to the Great Basin Science Sample and Records Library located in Reno, Nevada. The benefits resulting from this transfer were realized by the EM Nevada Program in terms of cost and space savings for geologic sample storage at the NNSS. The transfer did not impact the EM Nevada Program mission as access to representative geologic samples for regulatory and scientific purposes was preserved. Moreover, the Nevada Bureau of Mines and Geology acquired a significant resource of subsurface geologic samples and supporting technical data to support academic and scientific studies in a complex volcanic setting in southwest Nevada. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Low-Cost, Efficient and Durable High Temperature Wireless Sensors by Direct Write Additive Manufacturing for Application in Fossil Energy Systems

In-situ monitoring of physical parameters in fossil energy (FE) systems is challenging because of their high operating temperatures and oxidizing environments. This research aims utilize Aerosol Jet (AJ) nanoparticle printing, an advanced additive manufacturing method, to fabricate high temperature sensors for FE applications. Sensor films were fabricated by the AJ printing method with an excellent control over film microstructure and properties, in addition to creating less waste. Printing of six different materials, namely, silver (Ag), nickel (Ni), nickel-chromium (NiCr) alloy, copper (Cu), copper-nickel (CuNi), and carbon nanotubes (CNTs) was carried out for their evaluation of as sensor films. The films were then characterized using impedance spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS). Film microstructure was correlated with the sensor electrical and mechanical behavior at different temperatures (24-500 °C) and frequencies (0.02 - 300 kHz). This knowledge was then used to fabricate, test, and characterize strain and temperature sensors for high temperature applications. The strain sensors were shown to have a very low thermal strain up to 500 °C and a 57% higher gauge factor compared to the commercially available counterparts. This enhance performance was attributed to the inherent porosity in the sensor films that allowed the films to exhibit a metamaterial-like behavior when deformed. The AJ printed temperature sensors were shown to have the highest Seebeck coefficient amongst all the film-based temperature sensors. In addition, fundamental aspects of crystal structure and phase transformations in Ba(Fe0.7Ta0.3)O3-δ (BFTO), a high temperature sensor material, was investigated along with 3D printed antennas that can transmit sensor signals at high temperatures. Two PhD students, one from first generation college-going underrepresented minority community (URM), two postdoctoral fellows, and several masters and undergraduate students were supported by this award. This work opens up the possibility of realizing low-cost high-performance sensor devices using advanced manufacturing techniques for high temperature FE applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Efficient Quantum Gibbs Samplers with Kubo–Martin–Schwinger Detailed Balance Condition

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than being treated as an approximation to weakly coupled system-bath unitary dynamics. Recently, Chen, Kastoryano, and Gilyén (arXiv:2311.09207) introduced the first efficiently implementable Lindbladian satisfying the Kubo–Martin–Schwinger (KMS) detailed balance condition, which ensures that the Gibbs state is a fixed point of the dynamics and is applicable to non-commuting Hamiltonians. This Gibbs sampler uses a continuously parameterized set of jump operators, and the energy resolution required for implementing each jump operator depends only logarithmically on the precision and the mixing time. In this work, we build upon the structural characterization of KMS detailed balanced Lindbladians by Fagnola and Umanità, and develop a family of efficient quantum Gibbs samplers using a finite set of jump operators (the number can be as few as one), akin to the classical Markov chain-based sampling algorithm. Compared to the existing works, our quantum Gibbs samplers have a comparable quantum simulation cost but with greater design flexibility and a much simpler implementation and error analysis. Moreover, it encompasses the construction of Chen, Kastoryano, and Gilyén as a special instance.

97 MATHEMATICS AND COMPUTING↗