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A simplified economic model for inertial fusion.

ABSTRACT: A simple model for the levelized cost of electricity (LCOE) of an inertial fusion power plant is developed. The model has 14 parameters. These have been designed to be technology agnostic, such that the model may be applied broadly to all variants of inertial fusion. It is also designed to allow easy use of proxies from existing technology. The variables related most intimately to the physics challenges of inertial fusion, such as gain and target cost, are treated as parameters such that requirements can be found without bringing complex physics into the model. A Monte Carlo approach is taken to explore the parameter space. The most important conclusion is that a combination of high gain (greater than 500) and high fusion energy yield per shot (greater than 5?GJ) together appear to unlock more cost competitive designs than those in the existing literature. Designs with LCOE as low as $25/MWh are found with optimistic but not obviously unrealistic inputs. This article is part of a discussion meeting issue 'Prospects for high gain inertial fusion energy (part 1)'.

SUBMITTER: Hawker N

PROVIDER: S-EPMC7658748 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A simplified economic model for inertial fusion.

Hawker Nicholas N

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20201012 2184

A simple model for the levelized cost of electricity (LCOE) of an inertial fusion power plant is developed. The model has 14 parameters. These have been designed to be technology agnostic, such that the model may be applied broadly to all variants of inertial fusion. It is also designed to allow easy use of proxies from existing technology. The variables related most intimately to the physics challenges of inertial fusion, such as gain and target cost, are treated as parameters such that require ...[more]

PMID: 33040650

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Project description:In inertial body tracking, the human body is commonly represented as a biomechanical model consisting of rigid segments with known lengths and connecting joints. The model state is then estimated via sensor fusion methods based on data from attached inertial measurement units (IMUs). This requires the relative poses of the IMUs w.r.t. the segments-the IMU-to-segment calibrations, subsequently called I2S calibrations-to be known. Since calibration methods based on static poses, movements and manual measurements are still the most widely used, potentially large human-induced calibration errors have to be expected. This work compares three newly developed/adapted extended Kalman filter (EKF) and optimization-based sensor fusion methods with an existing EKF-based method w.r.t. their segment orientation estimation accuracy in the presence of model calibration errors with and without using magnetometer information. While the existing EKF-based method uses a segment-centered kinematic chain biomechanical model and a constant angular acceleration motion model, the newly developed/adapted methods are all based on a free segments model, where each segment is represented with six degrees of freedom in the global frame. Moreover, these methods differ in the assumed motion model (constant angular acceleration, constant angular velocity, inertial data as control input), the state representation (segment-centered, IMU-centered) and the estimation method (EKF, sliding window optimization). In addition to the free segments representation, the optimization-based method also represents each IMU with six degrees of freedom in the global frame. In the evaluation on simulated and real data from a three segment model (an arm), the optimization-based method showed the smallest mean errors, standard deviations and maximum errors throughout all tests. It also showed the lowest dependency on magnetometer information and motion agility. Moreover, it was insensitive w.r.t. I2S position and segment length errors in the tested ranges. Errors in the I2S orientations were, however, linearly propagated into the estimated segment orientations. In the absence of magnetic disturbances, severe model calibration errors and fast motion changes, the newly developed IMU centered EKF-based method yielded comparable results with lower computational complexity.

Dataset Information

A simplified economic model for inertial fusion.

Publications

A simplified economic model for inertial fusion.

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