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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HPE ultralit project (ARPA-E open program 2018) final report 15

The goal of the program was to build a fully integrated optical transceiver with >1 Tb/s and <1.5 pJ/bit operating at 50 ◦ C. Optical transceivers are critical components in high-performance-computers (HPC) and data centers, and the details of their implementation has a big impact on the total power consumption (energy efficiency) of an HPC system. Our proposed transceiver used three key enabling technologies. Firstly, SiGe avalanche photodetectors have record-low sensitivities, meaning that they can reach low bit error rates with very little light input. As a result, we can drive our lasers at a lower drive current, thus saving electrical power. Secondly, we use MOS-based capacitive tuning in our deinterleaver, modulator, and demultiplexer. Capacitive tuning allows for the tuning of photonic elements with zero static power consumption. Thirdly, we use quantum dots as the gain material in our light source. This allows us to efficiently use our light source at temperatures that are typically encountered in an HPCsystem. Our proposed optical transceiver consisted of a quantum dot comb laser as a light source, a booster SOA, MOS-based deinterleavers, MOS based ring modulators, MOS-based ring demultiplexers, and SiGe APDs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Prediction and uncertainty quantification of shale well performance using multifidelity Monte Carlo

Uncertainty quantification is an integral component of reservoir management, especially considering the inherent uncertainty in subsurface systems. While a standard practice to estimate the uncertainty, Monte Carlo (MC) simulation is computationally intense when the sampling population comprises high-fidelity simulations. Alternatively, the Multi-fidelity Monte Carlo (MFMC) simulation overcomes this computational intensity by integrating low- and high-fidelity simulations. Our goal is to minimize the number of expensive high-fidelity simulations while maintaining accuracy and using numerous fast and cheap low-fidelity simulations to efficiently sample to input parameter space of interest. We selected gas production from unconventional wells to demonstrate the potential speedups and accuracy of the MFMC approach. The model fidelity usually determines the trade-off between accuracy and efficiency. While the high-fidelity model is more accurate, the low-fidelity model is more efficient. Our high-fidelity simulation includes reservoir simulations of a hydraulically fractured well. On the other hand, our low-fidelity model comprises the parallel-plate flow model. We used differential programming to efficiently solve the 1D flow model, where automatic differentiation is used to efficiently compute the gradients. We matched the production profile of high-fidelity simulations with our low-fidelity simulations. Then, we used a support vector regression to map the high- and low-fidelity input parameters. The mapping function is essential to tune the low-dimensional parameter space of the low-fidelity model to the high-dimensional parameter space of the high-fidelity model. We found that we can use a combination of 9 high fidelity and 10,000 low fidelity simulations to efficiently and accurately simulate pressure management. This method is at least two orders of magnitude faster than only using high-fidelity simulations. Finally, from a broader perspective, MFMC could efficiently estimate the uncertainty of various systems and models, integrating low- and high-fidelity models.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2018

A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks

The increasing penetration of distributed generators (DGs) exacerbates the risk of voltage violations in active distribution networks (ADNs). The var capacity provided by DG inverters is a potential solution for voltage regulation. The conventional centralized control strategies limited by computation and communication burdens are difficult to meet the requirement of rapid voltage control. However, the local control strategies based on real-time measurements have a fast response to the frequent fluctuations of DG outputs. The performance of these local controllers depends on the tuning of the control parameters. This paper proposes a centralized-based method to determine the local voltage control strategies for DGs. A centralized parameter tuning model of control curves is built, in which and curves of DG inverters are mathematically formulated based on piecewise linearization. The original mixed-integer nonlinear programming (MINLP) model is converted into an effectively solved mixed-integer second-order cone programming (MISOCP) model using convex relaxation. The potential benefits of DG inverters are explored to regulate both reactive and curtailed active power, based on local voltage measurements. Case studies on a modified PG&E 69-node distribution system are carried out to verify the effectiveness of the proposed method. Results show that power losses of ADNs are significantly reduced and voltage profiles are also improved.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Neural networks as a control methodology

While conventional computers must be programmed in a logical fashion by a person who thoroughly understands the task to be performed, the motivation behind neural networks is to develop machines which can train themselves to perform tasks, using available information about desired system behavior and learning from experience. There are three goals of this fellowship program: (1) to evaluate various neural net methods and generate computer software to implement those deemed most promising on a personal computer equipped with Matlab; (2) to evaluate methods currently in the professional literature for system control using neural nets to choose those most applicable to control of flexible structures; and (3) to apply the control strategies chosen in (2) to a computer simulation of a test article, the Control Structures Interaction Suitcase Demonstrator, which is a portable system consisting of a small flexible beam driven by a torque motor and mounted on springs tuned to the first flexible mode of the beam. Results of each are discussed.

Mccullough, Claire L.↗
DOE OSTI2018

Envision Charlotte Project Final Report

The Envision Charlotte Project (EC Project), supported by partners at the Center for Climate and Energy Solutions, the University of North Carolina at Charlotte’s Center for Sustainably Integrated Buildings and Sites and the City of Charlotte, has successfully demonstrated that existing buildings can benefit from operations improvements and low-cost efficiency upgrades, and that behavioral change campaigns can be effectively designed for measurability, longevity and impact. Support from this grant gave Envision Charlotte and its partners the resources and flexibility to demonstrate a variety of approaches to improve coordination with stakeholders in the implementation of programs to reduce the consumption of energy, water and waste. The project team also gained insights into the motivations and abilities that influence stakeholder engagement in sustainability initiatives at multiple levels. Importantly, as this report describes, strong partnerships between nonprofits, industry partners and research institutions can produce tangible impacts and replicable results in building efficiency. Further, building operators working alongside student researchers can form productive relationships that can advance building energy management goals. Key Findings of the EC Project This project focused on advancing solutions to energy efficiency in U.S. commercial buildings. The Building Re-Tuning™ Training (BRT) developed by the U.S. DOE’s Pacific Northwest National Lab (PNNL), designed to detect energy savings opportunities and implement improvements with resources and online tools, provided the starting point for the curriculum developed by stakeholders in the EC Project. Although the project partners encountered some issues with data consistency, the overall project outcomes fell short of the energy savings stated in the original proposal, and the total participating building targets were lower than anticipated, important findings, outcomes and lessons learned were achieved. EC Project grantees observed that the PNNL BRT, as currently written, was not compatible with the skills of average frontline mechanical personnel in the Charlotte region where the grant program was performed. Frontline staff are not, typically, energy managers, engineers or building commissioning agents, but they can play a key role in building performance. As detailed in this report, the grantees therefore focused on changes to the BRT that improved the level of training and competency of the frontline workers in this region. Among the technical observations related to improving the BRT that were observed in the EC Project: centralized facilities management often has an impact on building performance; operators can be overly reliant on service contracts; individual behaviors matter, and technology cannot be viewed as a crutch; current Building Automation Systems (BAS) are often too complex to support building efficiency optimization, but BAS coupled with appropriate training can support better outcomes; the “perfectly optimized” building is not a near-term possibility, but better education, especially of executives and managers, on the role of behavioral intervention is a promising direction the building science community can and should pursue; a lack of standards and protocols, and competition between proprietary software from service contracts, remain challenges for this industry. These technical observations, in turn, informed the EC Project’s overall findings, which were: Accomplishing building efficiency is a process of education for all involved in the building design, construction and operation supply chain. Building operators, as a professional class, have an opportunity for further training and education that will lead to better on-the-job performance. An updated Building Re-Tuning™ Training curriculum, based on the findings of this report, could be an avenue for these operators’ professional development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The Kokkos Ecosystem [Brief]

In 2016/2017, the field of High-Performance Computing (HPC) entered a new era driven by fundamental physics challenges to produce ever more energy and cost-efficient processors. Since the convergence on the Message-Passing Interface (MPI) standard in the mid-1990s, application developers enjoyed a seemingly static view of the underlying machine — that of a distributed collection of homogeneous nodes executing in collaboration. However, after almost two decades of dominance, the sole use of MPI to derive parallelism acted as a limiter to improved future performance. While MPI is widely expected to continue to function as the basic mechanism for communication between compute nodes for the immediate future, additional parallelism is required on the computing node itself if high performance and efficiency goals are to be realized. When reviewing the architectures of the top HPC systems today, the change in paradigm is clear: the compute nodes of the leading machines in the world are either powered by many-core chips with a few dozen cores each, or use heterogeneous designs, where traditional CPUs marshal work to massively parallel compute accelerators which has as many as 200,000 processing threads in flight simultaneously. Complicating matters further for application developers, each processor vendor has its own preferred way of writing code for their architecture.The Kokkos EcoSystem was released by Sandia in 2017 to address this new era in HPC system design by providing a vendor independent performance portable programming system for scientific, engineering, and mathematical software applications written in the C++ programming language. Using Kokkos, application developers can be more productive because they will not have to create and maintain separate versions of their software for each architecture, nor will they have to be experts in each architecture's peculiar requirements. Instead, they will have a single method of programming for the diverse set of modern HPC architectures. While Kokkos started in 2011 as a programming model only, it soon became clear that complex applications needed more. It is also critical to have a portable mathematical functions and developers need tools to debug their applications, gain insight into the performance characteristics of their codes and tune algorithm performance parameters through automated processes. The Kokkos EcoSystem addresses those needs through its three main components: the Kokkos Core programming model, the Kokkos Kernels math library, and the Kokkos Tools project.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Extremum-seeking control for an Ultrasonic/Sonic Driller/Corer (USDC) driven at high-power

Future NASA exploration missions will increasingly require sampling, in-situ analysis and possibly the return of material to Earth for further tests. One of the challenges to addressing this need is the ability to drill using for low axial loading while operating from light weight platforms (e.g., lander, rover, etc.) as well as operate at planets with low gravity. For this purpose, the authors developed the Ultrasonic/Sonic Driller/Corer (USDC) jointly with Cybersonics Inc. Studies of the operation of the USDC at high power have shown there is a critical need to self-tune to maintain the operation of the piezoelectric actuator at resonance. Performing such tuning is encountered with difficulties and to address them an extremum-seeking control algorithm is being investigated. This algorithm is designed to tune the driving frequency of a time-varying resonating actuator subjected to both random and high-power impulsive noise disturbances. Using this algorithm the performance of the actuator is monitored on a time-scale that is compatible with its slowly time-varying physical characteristics. The algorithm includes a parameter estimator, which estimates the coefficients of a function that characterizes the quality factor of the USDC. Since the parameter estimator converges sufficiently faster than the time-varying drift of the USDC's physical parameters, the proposed extremum-seeking estimation and control algorithm is potentially applicable for use as a closed-loop health monitoring system. Specifically, this system may be programmed to automatically adjust the duty-cycle of the sinusoidal driver signal to guarantee that the quality factor of the USDC does not fall below a user-defined set-point. Such fault-tolerant functionality is especially important in automated drilling applications where it is essential not to inadvertently drive the piezoelectric ceramic crystals of the USDC beyond their capacities. The details of the algorithm and experimental results will be described and discussed in this paper.

Peek-seeking estimation and control↗
NASA NTRS1997

Performance Data Gathering and Representation from Fixed-Size Statistical Data

The two commonly-used performance data types in the super-computing community, statistics and event traces, are discussed and compared. Statistical data are much more compact but lack the probative power event traces offer. Event traces, on the other hand, are unbounded and can easily fill up the entire file system during program execution. In this paper, we propose an innovative methodology for performance data gathering and representation that offers a middle ground. Two basic ideas are employed: the use of averages to replace recording data for each instance and 'formulae' to represent sequences associated with communication and control flow. The user can trade off tracing overhead, trace data size with data quality incrementally. In other words, the user will be able to limit the amount of trace data collected and, at the same time, carry out some of the analysis event traces offer using space-time views. With the help of a few simple examples, we illustrate the use of these techniques in performance tuning and compare the quality of the traces we collected with event traces. We found that the trace files thus obtained are, indeed, small, bounded and predictable before program execution, and that the quality of the space-time views generated from these statistical data are excellent. Furthermore, experimental results showed that the formulae proposed were able to capture all the sequences associated with 11 of the 15 applications tested. The performance of the formulae can be incrementally improved by allocating more memory at runtime to learn longer sequences.

Yan, Jerry C.↗
NASA NTRS2018

Robotic External Leak Locator (RELL) Leak Plume Field Detection on the International Space Station (ISS)

The Robotic External Leak Locator (RELL) was deployed to the International Space Station (ISS) with the objective of demonstrating the ability to detect and locate small leaks. On-orbit operations began in late November 2016 and following scanning activities to characterize the natural and induced environment of the ISS, RELL focused on the United States External Active Thermal Control System (EATCS). RELL successfully detected ammonia related to a known small ammonia leak in the port-side EATCS, with the highest pressure values around the inboard Radiator Beam Valve Module 1 (RBVM 1). An additional day of scanning was subsequently performed in December 2017 to focus on RBVM 1. RELL was approved for additional external operations in February 2017 with the goal of fine tuning the location of the leak. Using grid scanning patterns, RELL detected ammonia around RBVM 1 and located the approximate source of the leak. The potential leak site was inspected by a crew member during an Extravehicular Activity (EVA) in March 2017, and the suspected radiator-side lines were isolated from the port-side EATCS coolant loop in April 2017. Subsequent monitoring of the system pressures showed that the leak has stopped, indicating RELL accurately located the source of the EATCS leak. These activities verify that RELL enhances the ISS Program's ability to not only locate small leaks, but isolate the source with minimal impact to the entire ISS system.

External Active Thermal Control System↗
DOE OSTI2022

Feedback Control Approaches for Restoration of Power Grids from Blackouts

The automated restoration of power systems with variable energy resources is a timely problem to tackle. Automated restoration advice can support operators in deciding on strategic actions to restore power grids from a blackout with a mix of conventional and renewable generation resources. To this end, this paper frames the restoration process of power grids with solar resources as a nonlinear dynamic model with algebraic constraints in discrete time which is steered by feedback control loops. We discuss two feedback-control strategies based on greedy and reinforcement learning algorithms, and contrast their performance with restoration plans generated by a mixed-integer linear program. We found that the reinforcement learning algorithm infers restoration actions faster than the greedy one. However, the tuning process of the reinforcement learning parameters is slower than for the greedy one.

machine learning↗
NASA NTRS2005

Software Would Largely Automate Design of Kalman Filter

Embedded Navigation Filter Automatic Designer (ENFAD) is a computer program being developed to automate the most difficult tasks in designing embedded software to implement a Kalman filter in a navigation system. The most difficult tasks are selection of error states of the filter and tuning of filter parameters, which are timeconsuming trial-and-error tasks that require expertise and rarely yield optimum results. An optimum selection of error states and filter parameters depends on navigation-sensor and vehicle characteristics, and on filter processing time. ENFAD would include a simulation module that would incorporate all possible error states with respect to a given set of vehicle and sensor characteristics. The first of two iterative optimization loops would vary the selection of error states until the best filter performance was achieved in Monte Carlo simulations. For a fixed selection of error states, the second loop would vary the filter parameter values until an optimal performance value was obtained. Design constraints would be satisfied in the optimization loops. Users would supply vehicle and sensor test data that would be used to refine digital models in ENFAD. Filter processing time and filter accuracy would be computed by ENFAD.

Chuang, Jason C. H.↗
DOE OSTI2023

FY23 Progress on Computational Modeling of the Water-Based NSTF

This report summarizes the system-level modeling effort by Argonne National Laboratory (Argonne) of the Natural convection Shutdown heat removal Test Facility (NSTF) in FY23. As a continuation of the modeling effort from FY22, this year’s work focuses on improving the RELAP5-3D model developed previously for two-phase flow simulations. The RELAP5-3D model is updated to more accurately capture the heat loss experienced by the facility. The updated model is compared against experimental data for benchmarking purposes of the RELAP5-3D input model. By correctly accounting for heat loss, the updated RELAP5-3D model can now predict the two-phase baseline case more accurately. The onset and the duration of instability are captured well by the model. Furthermore, analyses are performed to better understand the instability mechanism experienced by the flow where the expansion of the boiling boundary in the chimney is studied in details and the fundamental frequencies of the oscillations are obtained. The updated RELAP5-3D model is further compared against four fault conditions, namely the reduction of riser header inlet flow area, depletion of system inventory, blocked riser channels, and static boiling scenario. For each fault condition, minor modifications and tuning are performed to improve the predictions of the model. The purpose of the analyses is to investigate the capability of RELAP5-3D in predicting complex two-phase flows in possible accident scenarios in actual Reactor Cavity Cooling System (RCCS). Overall, the model is able to capture the behaviors and trends of these fault conditions relatively well. Some discrepancies remain between the experimental data and the predictions, many of which are likely due to the differences in the predicted and experimental vapor generation rate. Future work will focus on the continued development of the current RELAP5-3D input model of the NSTF to both improve the accuracy of the model’s predictive capability and continue supporting the experimental program needs. The mutually beneficial relationship between analysis and experimental efforts has become integral to the parent NSTF program, and the greater objective to fully understand and accurately predict the heat removal performance of a full scale RCCS concept.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

City-level impacts of building tune-ups: Findings from Seattle's building tune-ups program

Many U.S. cities are implementing policies to reduce greenhouse gas (GHG) emissions of their buildings. These range from building energy benchmarking and disclosure to building performance standards (BPS) that require buildings to meet specific targets of energy use or emissions. The City of Seattle adopted a climate action plan in 2013 that set a goal of zero net GHG emissions in the road transportation, buildings, and waste sectors by 2050, with a number of near and long term actions. Seattle implemented mandatory building tune-ups in 2016, applying to commercial buildings larger than 50,000 sqft. Building tune-ups1 involve assessment and implementation of operational and maintenance (O + M) improvements to achieve energy and water efficiency, such as changes to thermostat set points or adjusting lighting or irrigation schedules. Seattle's tune-ups program covered 27 such improvements in HVAC, lighting, domestic hot water, and envelope systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Evaluating and Optimizing OpenCL Base64 Data Unpacking Kernel with FPGA

Development of applications using OpenCL targeting FPGAs is an emerging approach on heterogeneous computing systems. This paper uses the data unpacking algorithm in Base64 encoding as a case study to present programming and optimization techniques, and experimental results of the OpenCL-based implementations on an FPGA. We explain the algorithm and evaluate the performance of the kernel implementations with Intel's FPGA OpenCL SDK. The experimental results show kernel vectorization and duplication are two optimization techniques that can improve the kernel performance. The performance of kernel duplication is also closely related to the local work size. Our experiment shows 16-lane vectorization increases the bandwidth by a factor of 2 to 10 for large input data sizes. Moreover, the performance of kernel duplication using 16 compute units is 40% to 1.5% less than that of kernel vectorization depending on the input size. Tuning the local work size can improve the kernel performance by a factor of 3 to 23. For this kernel, using local memory is not an effective technique to improve the kernel performance because input data is not reused. A combination of vectorization and duplication achieves the highest performance of 12.3 GiB/s. Compared to an Intel Xeon E5 CPU and an Nvidia Tesla K80 GPU, the performance of the kernel on the Arria 10 FPGA is 6.7X faster than the CPU and 3X slower than the GPU. The performance per watt on the FPGA is 20.5X higher than the CPU and 1.19X lower than the GPU.

Base64 Encoding↗
DOE OSTI2026

TChem-atm (v2.0.0): scalable performance-portable multiphase atmospheric chemistry

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high-performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. In a proof-of-concept integration with the particle-resolved model PartMC, TChem-atm reproduces the existing PartMC–CAMP implementation within solver tolerances and delivers substantial GPU speedups, especially for large particle populations. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. TChem-atm enables performance-portable execution across CPUs and GPUs, though optimal efficiency may require modest architecture-specific tuning (e.g., team and vector sizes), with up to a twofold improvement on the NVIDIA H100. It directly supports sectional and particle-resolved host models, while modal aerosol schemes require minor adaptation to provide particle-scale quantities such as representative diameters. By enabling chemically detailed, multiphase simulations with performance portability and host-model flexibility, TChem-atm facilitates the incorporation of advanced chemistry into atmospheric models.

Díaz-Ibarra, Oscar Homero [Sandia National Laborat↗
DOE OSTI2020

Assessments of data centers for provision of frequency regulation

There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. Here, when chillers are deactivated, both strategies have a similar regulation capacity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Preliminary Findings of the Experimental Development Unit Cold Flow Test for a Generation Zero Nuclear Propulsion Engine

Nuclear thermal propulsion (NTP) technology will greatly benefit human travel to Mars by significantly shortening transit times, improving crew safety, and providing more mission flexibility than traditional chemical rockets. As part of DRACO follow-on work to develop, build, and fly a generation zero NTP engine, a full scale flight-like experimental design unit (EDU) reactor was constructed to collect sufficient on-ground performance data to characterize flow induced vibrations (FIV) of critical reactor structures/components, inform development of the engine and reactor control algorithm, and collect pressure drop and flow distribution data across the reactor. The fluid conditions for the test program were designed to achieve system responses equivalent to that of an operational engine through all phases of engine operation including reactor startup, mainstage operation, reactor shutdown, and reactor cooldown. Over 100 tests were executed, flowing either GN2 or GHe through the EDU at varying flow rates and pressures. This experiment provided early validation of flow behavior and vibration risks before nuclear testing, boosted critical subsystem TRLs, informed design iterations, and reduced future test costs. The steady-state flow parameters for the experiment were modeled in Ansys Thermal Desktop, allowing rapid tuning and experiment-informed updates to a flight-like test matrix. The EDU dynamic environment was characterized with accelerometers, strain gauges, and high-frequency pressure transducers all sampled at 20 kHz. While many narrow-band oscillations were identified, no significant FIV occurred; the reactor structural responses tend to be enveloped by typical launch vehicle ascent vibration environments (defined up to 2 kHz), although significant energy is also present at higher frequencies.

Flow Induced Vibration↗
DOE OSTI2021

Design principles of tandem cascade photoelectrochemical devices

Cascade photoelectrocatalysis (PEC) is a possible method to improve the selectivity of solar-driven CO 2 reduction (CO 2 R). This concept can be realized by coupling different CO 2 R catalysts to different subcells in a multijunction photovoltaic (PV) stack. Efficient implementation will require careful tuning of the photocurrents and design of the photovoltages provided by the subcells to the CO 2 R catalysts in such a way as to facilitate the target reaction. Here, we outline the design principles of the tandem PEC approach using two-step conversion of CO 2 to ethylene in aqueous electrolyte, via a CO intermediate, as a model system. To perform this reaction, the first coupled PV-catalyst component should provide 4 electrons to reduce 2 molecules of CO 2 to CO; the second component should provide 8 electrons to reduce 2 CO molecules to C 2 H 4 . Based on known CO 2 R catalysts, the overpotential required to produce CO can be less than that required to reduce it to ethylene, creating the opportunity for improved efficiency. Cascade PEC can be realized in a three-terminal tandem (3TT) configuration using III–V-semiconductor based subcells coupled to Au (produces CO intermediate) and Cu (converts CO to ethylene). The current to each catalyst can be controlled by the area of the subcell exposed to the electrolyte, and the photovoltage is determined by the materials selected and device configuration. Operating conditions are found by simulating the coupled system using the open-source circuit simulator SPICE (Simulation Program with Integrated Circuits Emphasis). We identify conditions under which a 3TT configuration can have a higher solar to chemical conversion efficiency compared to a two-terminal two-junction tandem (2T 2J) with the same absorbers and a Cu catalyst only. We also show that 3TT PEC devices can be less sensitive to variations in catalyst activity compared to 2T devices. Lastly, we discuss the applications of cascade PEC to CO 2 reduction, using different intermediates, and to other chemical networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗